Reliable Signal Research Articles

MREIFlow: Unsupervised dense and time-continuous optical flow estimation from image and event data

Objectives:We focus on exploring an unsupervised learning-based model which can take advantage of a single image and events to estimate dense and time-continuous optical flow. Methods:We propose a multi-scale optical flow recurrent estimation network, called MREIFlow, which mainly consists of a triplet feature encoder, a feature fusion subnetwork, and a flow iterative decoder. The triplet feature encoder is capable of extracting multi-scale features from a single image and events. The feature fusion subnetwork is designed to integrate spatial–temporal information in features and generate pseudo-features. The flow iterative decoder can perform feature correlation and estimate full-resolution optical flow iteratively in a coarse-to-fine way. We train the network in an unsupervised learning way to avoid using labeled data. In order to provide plentiful and reliable supervisory signals, we design a hierarchical unsupervised learning scheme. In the scheme, the multi-level optimization objectives are designed to supervise network training in different aspects. We also introduce a teacher model to provide auxiliary supervision. Besides, we apply a loss selection strategy to mine promising supervisory signals. Results:Experiments on the MVSEC dataset show that the proposed method achieves remarkable performance under indoor and outdoor circumstances. Compared with previous methods, the proposed method can provide dense and time-continuous optical flow estimation. Conclusion:The proposed network can take advantage of a single image and events to produce dense and time-continuous optical flow estimation, and can be trained through unsupervised learning techniques. However, it requires high computation and memory to guarantee the precision of optical flow. To address this problem, we will explore an efficient and lightweight network architecture in the future.

Depth dimension in seepage detection: Insights for exploration and geological gas storage surveillance

The search for surface hydrocarbon seeps has historically served as a fundamental tool in the early stages of oil and gas exploration. However, as easily detectable surface hydrocarbon seeps become increasingly scarce, advancement in analytical capabilities have shifted the focus towards detecting ultra-trace levels of hydrocarbons, commonly referred to as microseepages. While it is critical to understand the distribution of hydrocarbon microseepage for assessing petroleum systems and optimizing exploration efforts, the role of sampling depth in the detection of microseepage remains poorly understood. In this study, we investigated the influence of sampling depth on microseepage detection, using passive soil-gas sampling techniques at depths ranging from 0.5 to 10 m at sites with hydrocarbon-bearing reservoirs and dry wells. Organic geochemical analysis, including thermal desorption-gas chromatography-mass spectrometry (GC-MS), was employed to identify and quantify hydrocarbon and nonhydrocarbon compounds.The study result demonstrates that sampling at the Minimal Interference Zone (MIZ) depths of 5 m or greater significantly mitigates the risk of interference signals, thereby enabling the identification and quantification of hydrocarbon microseepages at trace and ultra-trace concentrations. At these depths, the attenuation of non-hydrocarbon compounds was observed, making the hydrocarbon signal markedly more distinguishable. Geochemical characterization of the samples revealed spatial and vertical heterogeneity in the distribution of hydrocarbon compounds, indicative of variations in the microseepage mechanisms potentially influenced by factors such as sedimentary basin settings and secondary alterations associated with the vadose zone. By utilizing passive soil-gas sampling techniques coupled with GC-MS, this study emphasizes the importance of sampling depth to improve the detection of reliable microseepage signal while reducing interference and background noise. The proposed technique not only refines existing methodologies for petroleum system assessments but also paves the way for broader geological applications. Specifically, the approach offers critical insights for the development of high-resolution geochemical surveys adaptable for surface background profiling in CO2 sequestration projects to monitor for storage efficiency and seal integrity.

Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation

This study evaluates the performance of different classification methods in classifying healthy individuals and stroke patients. The hand gesture variations of the subjects were also analyzed based on electromyography (EMG) signals. Several classification methods were tested in this analysis to find out which method had the most suitable performance. The results showed that Decision Tree and Naive Bayes classifiers achieved the highest performance in classifying EMG signals from healthy individuals and stroke patients, with both methods showing high accuracy, precision, recall, and F1 score. Specifically, Decision Tree excelled in overall accuracy and recall, while Naive Bayes showed superior precision. For hand gesture recognition, SVM, KNN, and Random Forest classifiers showed similarly high performance, achieving accuracy, precision, recall, and F1 score above 82%. Naive Bayes also performed well, especially in precision, while Decision Tree performed poorly compared to other methods. This insight can form the basis for the development of more effective and personalized rehabilitation systems for stroke patients, by utilizing reliable and accurate EMG signal classification

Highly Adhesive and Stretchable Epidermal Electrode for Bimodal Recording Patch.

For numerous biological and human-machine applications, it is critical to have a stable electrophysiological interface to obtain reliable signals. To achieve this, epidermal electrodes should possess conductivity, stretchability, and adhesiveness. However, limited types of materials can simultaneously satisfy these requirements to provide satisfying recording performance. Here, we present a dry electromyography (EMG) electrode based on conductive polymers and tea polyphenol (CPT), which offers adhesiveness (0.51 N/cm), stretchability (157%), and low impedance (14 kΩ cm2 at 100 Hz). The adhesiveness of the electrode is attributed to the interaction between catechol groups and hydroxyls in the polymer blend. This adhesive electrode ensures stable EMG recording even in the presence of vibrations and provides signals with a high signal-to-noise ratio (>25 dB) for over 72 h. By integrating the CPT electrode with a liquid metal strain sensor, we have developed a bimodal rehabilitation monitoring patch (BRMP) for sports injuries. The patch utilizes Kinesio Tape as a substrate, which serves to accelerate rehabilitation. It also tackles the challenge of recording with knee braces by fitting snugly between the brace and the skin, due to its thin and stretchable design. CPT electrodes not only enable BRMP to assist clinicians in formulating effective rehabilitation plans and offer patients a more comfortable rehabilitation experience, but also hold promise for future applications in biological and human-machine interface domains.

High Throughput Screening of Electroactive Bacteria Using a Cross-Bar Architecture in a 3D Design Array

Promoting extracellular electron transfer (EET) in bacteria has many widespread applications including wastewater treatment and environmental remediation. With the development of synthetic biology technologies that can alter microbial electron transfer routes and enhance their electrogenic capacity, there is a need for high throughput systems to identify the responsible genes and the consequent metabolic pathways in an expeditious way. Conventional platforms utilizing single electrodes, or electrochemical cells, and colorimetric detection suffer from low sensitivity and low throughput. Moreover, they require bulky equipment for readout and fluid handling. Moving towards a nuanced miniaturized electrochemical detection, we propose individually addressable microwells used to monitor electron flux from EET-capable bacteria with the ability to screen a large number of electroactive bacteria for various applications. Materials and method : The device is simple and fabricated with cost-effective electrodes. Carbon felt is used as the working electrode to improve bacterial entrapment and assist in capturing maximum electrons donated by the electroactive bacteria. Silver-silver chloride ink (Ag/AgCl) as the reference electrode will help maintain the potential of with respect to the working electrode. Conductive carbon ink as the counter electrode will promote current collection. The proposed device has the elements of a traditional three electrode system arranged in two different planes resulting in a 3D configuration of electrodes. The reference and counter electrode are on the bottom plane with acrylic wells housed right above each electrode pair, while the working electrode is used in the cross-bar architecture from the top plane as shown in Figure 1(a). The fabrication steps are illustrated in Figure 1(b). This system can be scaled to have 10000 microwells on a single platform with individual addressability of each micro-well array. With this layout, connections from the innermost wells can be drawn effortlessly without any signal damping from the surrounding wells. As a proof of concept, we have fabricated a 9 by 9 array (20cm by 20 cm) as shown in Figure 1(a). Five strains of Shewanella oneidensis MR-1, the wild type organism and four modified strains overexpressing variants of MtrA (unmodified MtrA, IV-205, IV-261 and an empty vector), were screened for their current producing capacity [1]. Results:A bacterial load of OD600= 0.065 was sufficient to give a reliable current signal. For each strain, the working electrode was biased at 0.205 V with respect to the reference electrode (Ag/AgCl), and the resulting chronoamperometry signal was recorded as shown in Figure 1(c). The values obtained helped identify the current-producing capacity of each mutant. Among them, the highest peak current was given by mtr A+ (20 μA), and IV – 261 gave a low peak current (2 μA) within 3 minutes, validating the difference in the genetic make-up and EET capabilities. Technical and biological replicates were also conducted for all the 5 strains. The average standard deviation for the technical and biological replicates (n=3, OD600=0.065) was 0.09 μA and 0.56 μA respectively. A similar current trend for the same set of strains was obtained by Ian et al., [1] using a traditional bioelectrochemical system, thus validating our platform’s functionality and integrity. This system allows for parallel screening of wild type and mutant variations of multiple electroactive bacteria as demonstrated. The high throughput feature enabled by this device can also find application to characterize mutants generated by directed evolution workflows. Additionally, mechanical and electrical multiplexing can further improve the electronic instrumentation and connections between the wells for parallel readout and will also minimize human intervention during the current measurements. Chronoamperometry measurements is an important technique to characterize EET. Thus, a low-cost system like ours will help do that in a shorter time frame with minimal bacterial inoculum and for a larger number of electroactive bacterial species. In conclusion, we aim to show that a 3D carbon felt platform is miniaturized, and can be scaled to understand and characterize EET from electroactive bacteria in a high throughput manner. References Ian J. Campbell, Joshua T. Atkinson, Matthew D. Carpenter, Dru Myerscough, Lin Su, Caroline M. Ajo-Franklin, and Jonathan J. Silberg Biochemistry 2022 61 (13), 1337-1350 Figure 1

#Fakefamous: how do influencers use disinformation to establish long-term credibility on social media?

PurposeCredible influencers play a key role in shaping the views and preferences of social media users. However, many influencers intentionally use disinformation (e.g. false narratives) to deceive users and gain their trust. This can have serious repercussions, not only for the firms that associate with these influencers but also for users. Further, and alarmingly, many influencers who use disinformation can sustain their credibility over time. This research explores how influencers use disinformation to establish long-term credibility on social media.Design/methodology/approachDrawing on self-presentation theory, we use an in-depth qualitative case study to address our research question, primarily relying on archival data obtained from multiple sources.FindingsOur findings suggest that three stages of self-presentation are required to establish influencer credibility based on disinformation: backstage (preparing to deceive), experimentation (testing deception), and frontstage (launching deceptive ideas on a large scale). We also find that when fraudulent influencers simultaneously weaponise a counterculture and mindfully encase disinformation, users view them as highly credible.Practical implicationsWe offer practical suggestions for regulating fraudulent influencers, including enacting fact-checking procedures, using IT artefacts as reliability signals, and building awareness programmes to develop vigilance in social media communities.Originality/valueWe contribute to self-presentation theory by adding experimentation as a critical stage in developing disinformation that works for long periods. We also contribute to the literature at the intersection of social media influencers and disinformation research by revealing why social media users believe in fraudulent influencers.

Nerve entry points in the mimic musculature of the horse head.

Facial expressions are important in pain recognition in horses, but current observation-based pain scales remain subjective. A promising technique to quantitatively measure subtle changes in expression patterns, including changes invisible to the human eye, is surface electromyography (sEMG). To achieve high-quality and reliable sEMG signals, unilateral placement of bipolar electrodes is required in relation to the motor endplates (MEP). We aimed to localize the nerve entry points (NEPs; where the nerve branch first pierced the muscle belly) and the direction of the terminal nerve endings to estimate MEP locations of the innervating nerves in five equine facial muscles involved in pain expression. Three cadaveric Dutch Warmblood horse heads were dissected to identify the NEPs in the musculi caninus, levator anguli oculi medialis, nasolabialis, masseter and zygomaticus. These points were marked with pins and measured in relation to a reference line between two anatomical landmarks near the origin and insertion of the respective muscle. Relative distances were calculated from the most caudally situated landmark. NEPs were located at 33%-38% (caninus), 69%-86% (levator anguli oculi medialis) and 0%-18% (zygomaticus) from the caudal landmark. The nasolabialis showed two innervations zones. Its NEPs were located at 47%-72% (dorsal muscle branch) and 52%-91% (ventral branch). All terminal nerve endings were found to run in rostral direction. The masseter showed numerous NEPs diffusely spread within the muscle belly. Therefore, calculation of relative positions was not performed. These results could form the basis for feasibility studies and standardization of bipolar electrode positioning invivo to measure facial muscle activity patterns in horses.

Evaluating the influence of digital strategy on the interplay between quality certification and sales performance using data science and machine learning algorithms

In the context of international trade with information asymmetries, business certifications can serve as signals of product quality and reliability for customers and stakeholders. Recognizing this, the current study unbraces “signalling theory” to explore the impact of digital transformation strategy (DTS) on firm’s international quality certification status and sales performance in the global economy. To this end, treating quality certification endogenously, the study relies on bivariate copula regression modeling of 121429 firms’ responses data extracted from the World Bank’s Enterprise Surveys. Consistent with the hypothesized theoretical predictions, the findings revealed that DTS, in the form of website ownership, has a significantly positive impact on sales performance due to increasing firm’s reach, visibility, engagement, conversion, and retention of customers across different channels and touchpoints. As theoretically conjectured, quality certification is also found to positively impact sales performance by enhancing the firm’s reputation, trustworthiness, and attractiveness in the global marketplace. Most importantly, the study found empirical evidence that DTS significantly moderates the relationship between quality certification and sales performance, such that quality certification has a stronger effect on sales performance for firms with a well-defined DTS (web=1) than those without (web=0); leading therefore to the conclusion that DTS can amplify the signal of quality certification by making it more visible, transparent, and consistent across different digital channels and touchpoints. Hence, the study provides valuable empirical and theoretical insights that inform current business practices, future research, and potential policy measures to support business growth, and enhanced competitiveness in the global market place.

An early warning indicator trained on stochastic disease-spreading models with different noises.

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Research on remote reference denoising method based on non-coaxial and non-coplanar tunnel NMR detection

Abstract The limited space within the tunnel constrains the size of the antenna for NMR detection, thereby significantly impacting the signal-to-noise ratio (SNR) of NMR signals. Insufficient SNR data poses substantial challenges to obtaining reliable NMR signals. The paper presents a novel approach to address the challenge of strong background noise in tunnel environments and low SNR data by incorporating the ground multi-channel remote reference denoising method into tunnel NMR advance detection. Specifically designed for narrow tunnels, a multi-channel non-coaxial and non-coplanar remote reference denoising method is proposed. Firstly, the effectiveness of the non-coaxial, non-coplanar remote reference denoising method is verified in the laboratory environment. Secondly, the correlation between the detector antenna and the reference antenna is calculated theoretically to ensure the significant correlation between the detector antenna and the reference antenna. Finally, two processing methods of reference denoising and non-reference denoising are carried out respectively by combining the tunnel detection data. By comparing the inversion results and the engineering construction results, the effectiveness of non-coaxial and non-coplanar remote reference denoising methods in tunnel NMR detection is proved, which provides relevant research support for expanding the application of tunnel NMR detection technology.

Lignin Nanosphere‐Modified MXene Activated‐Rapid Gelation of Mechanically Robust, Environmental Adaptive, Highly Conductive Hydrogel for Wearable Sensors Application

AbstractAdvanced conductive hydrogels demonstrate substantial potential for wearable devices. Nevertheless, the transformative advance in soft electronics raises harsh requirements on the hydrogel candidates, such as rapid and on‐site fabrication, mechanical flexibility, high sensitivity, and wide use temperature. Here, this problem is overcome by incorporating a dual catalytic system based on lignin‐modified MXene‐Fe3+ into commercial hydrogels. This system 1) can form a composite hydrogel in a time scale of min at ambient condition without the supply of external energy, 2) incorporates multiple enhanced strategies into polymer chains, and 3) constructs well‐organized hybrid conductive network. The fabricated hydrogel displays an improved and balanced overall performance, including high ductility (2139%), moderate electrical conductivity, and strong temperature tolerance (−70–50 °C). Combined with the great merits of above performance, the hydrogel‐based sensor with good sensing (maximum GF: 2.8), stable repeatability (200% for 200 cycles), and wide work window of 0%–947%, thereby disclosing promising application in physiological movements, such as motion recognition and breathing state detection. Sensationally, even in complex or harsh surroundings, the sensors also produce stable and reliable signal output. Together, the strategy provides a new mentality of designing hydrogel materials for booming and advanced wearable electronics.

A MOFs‐Derived Hydroxyl‐Functionalized Hybrid Nanoporous Carbon Incorporated Laser‐Scribed Graphene‐Based Multimodal Skin Patch for Perspiration Analysis and Electrocardiogram Monitoring

AbstractAlthough metal‐organic framework (MOF)‐derived nanoporous C (NPC) materials offer several advantages for electrochemical sensor applications, surface functionalization and porosity tuning can affect sensor performance. This study presents the development of a skin patch for perspiration and electrocardiogram (ECG) monitoring, leveraging the unique properties of MOF‐on‐MOF‐derived surface‐functionalized hybrid nanoporous C (f‐HNPC) incorporated into laser‐scribed graphene (LSG). Hydroxyl (OH) group‐functionalized NPC, achieved through KOH activation, facilitates electron transport at the electrode–electrolyte interface. This enhances the electrochemical activity, thereby improving sensor sensitivity and expanding the detection range. The integration of f‐HNPC provides enhanced surface area and electrochemical properties, enabling sensitive and selective detection of sweat biomarkers, including glucose (103 µA mM−1 cm−2) and uric acid (184 µA mM−1 cm−2) along with an ultra‐wide glucose detection range (up to 41.5 mM). Moreover, the incorporation of LSG ensures excellent mechanical flexibility, facilitating conformal contact with the skin for reliable signal acquisition. The proposed skin patch demonstrates promising performance in real‐time perspiration analysis and ECG monitoring with a signal‐to‐noise ratio of 23.63 dB, along with high stability and long‐term durability. The synergistic combination of f‐HNPC and LSG shows great potential for developing advanced wearable biosensing platforms for personalized healthcare applications.

Selective consolidation of learning and memory via recall-gated plasticity.

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

ReCRec: Reasoning the Causes of Implicit Feedback for Debiased Recommendation

Implicit feedback ( e.g ., user clicks) is widely used in building recommender systems (RS). However, the inherent notorious exposure bias significantly affects recommendation performance. Exposure bias refers a phenomenon that implicit feedback is influenced by user exposure, and does not precisely reflect user preference. Current methods for addressing exposure bias primarily reduce confidence in unclicked data, employ exposure models, or leverage propensity scores. Regrettably, these approaches often lead to biased estimations or elevated model variance, yielding sub-optimal results. To overcome these limitations, we propose a new method ReCRec that Re asons the C auses behind the implicit feedback for debiased Rec ommendation. ReCRec identifies three scenarios behind unclicked data — i.e. , unexposed, dislike or a combination of both. A reasoning module is employed to infer the category to which each instance pertains. Consequently, the model is capable of extracting reliable positive and negative signals from unclicked data, thereby facilitating more accurate learning of user preferences. We also conduct thorough theoretical analyses to demonstrate the debiased nature and low variance of ReCRec. Extensive experiments on both semi-synthetic and real-world datasets validate its superiority over state-of-the-art methods.

Conjugation Linked PEDOT:PSS with Low Impedance and High Stretchability for Epidermal Electrophysiology.

Long-term epidermal recording of bioelectricity is of paramount importance for personal health monitoring. It requires stretchable and dry film electrodes that can be seamlessly integrated with skin. The simultaneous achievement of high conductivity and skin-like ductility of conducting materials is a prerequisite for reliable signal transduction at the dynamic interface, which is also the bottleneck of epidermal electrophysiology. Here, carbon nanotubes (CNTs) are introduced as "conjugation linkers" into a topologically plasticized conducting polymer (PEDOT:PSS). A thin-film electrode with high conductivity (≈3250Scm-1) and high stretchability (crack-onset strain>100%) is obtained. In particular, the conjugation linker enables the high volumetric capacitance and the low film resistance, both of which synergically reduce the interfacial impedance. The capabilities of this electrode is further demonstrated in the precise recording of various electrophysiological signals.

A highly efficient homogeneous electrochemiluminescence aptasensor for amplified analysis of Ochratoxin A

Ochratoxin A (OTA) is a toxic thermostable subordinate metabolite produced by Aspergillus and Penicillium species. OTA contamination occurs in a wide range of foods and poses a threat to human health. Therefore, it is crucial to construct a sensitive and reliable method for analyzing OTA to ensure food safety. Herein, a simple yet sensitive ECL aptasensor has been constructed for OTA detection through the synergistic amplification between catalytic DNA assembly (CDA) and exonuclease III (Exo III)-initiated recycling reaction. The anti-OTA aptamer-encoded sensing unit specifically recognizes target OTA and leads to the release of the trigger sequence for stimulating the CDA circuit, yielding numerous dsDNA hybrids that carry the re-assembled S strand. Each of the as-assembled S sequence could hybridize with the Ru(bpy)32+-labeled substrate (S*-Ru) to produce a T-shape structure with a blunt 3′ terminus, which can be hydrolyzed and digested by Exo III, leading to the liberation of mononucleotide functionalized with Ru(bpy)32+ (Ru). Simultaneously, the S sequence was released and hybridized with another S*-Ru sequence for initiating the subsequent cycle of Exo III-stimulated digestion process until all the S*-Ru strands are digested, thus resulting in the generation of numerous Ru and remarkably amplified ECL signals for high-performance detection of OTA. With reliable analyte recognition and high signal gain, the ECL system enabled the high-performance and accurate determination of OTA in buffer, wheat, and wine samples, thus showing considerable potential applications for highly efficient determination of contaminants of low concentration in foodstuff.

P-112 Magnetic resonance spectroscopy at the micro scale (Micro-MRS) for non-invasively assessing individual mammalian embryos’ metabolism

Abstract Study question Can Micro-Magnetic Resonance Spectroscopy (micro-MRS) reveal the metabolic fingerprint of mammalian pre-implantation embryo? Summary answer Our research demonstrates that rapid, non-invasive analysis of embryos using Micro-Magnetic Resonance Spectroscopy (micro-MRS) can effectively uncover the significance of embryonic metabolites during development. What is known already Non-invasive selection of optimal embryos for transfer is a crucial challenge in assisted reproduction for both humans and animals. Magnetic Resonance Spectroscopy (MRS) emerges as a promising solution due to its chemical sensitivity and non-invasive nature. Despite its effectiveness in analysing large organisms, MRS faces obstacles in small sample handling and sensitivity, particularly with embryos, limiting its clinical and research applications. Our study overcomes these challenges using innovative microchip-based MRS sensors. This approach enabled successful MRS at the embryo scale, revealing metabolic biomarker linked to developmental potential in pre-implantation bovine embryos and opening new possibilities in assisted reproduction technology. Study design, size, duration Multiple MRS measuring sessions were conducted on two distinct embryo cohorts. The first cohort involved naturally arrested IVP embryos at early (8-cell) and late (morula/blastocyst) stages, establishing metabolic biomarkers in mammalian pre-implantation embryos. The second cohort involved 8-cell embryos which were further cultured over 10 days post-MRS with imaging at specific intervals (3 hours post-thawing, days 5, 8, and 10). This step was crucial to back-correlate biomarkers with in-vitro development based on bright field microscopy. Participants/materials, setting, methods Both studies involved vitrified samples that were subjected to a 50-minute MRS measurement following a 3-hour post-warming culture period. Single bovine embryo analysis was performed using our 1nL micro-MRS probe. Pre-implantation bovine embryos were obtained through IVF (n = 32 samples for cohort one and, n = 61 for cohort two). Post-MRS, the embryos were cultured in equilibrated IVC medium in a humidified incubator with 5% CO2 and 6% O2 at 38 °C. Statistical significance was set at p < 0.05. Main results and the role of chance Micro-Magnetic Resonance Spectroscopy (Micro-MRS) was utilized to identify metabolic markers in single bovine embryos, providing insights into their pre-implantation developmental stages. The spectroscopy data revealed distinct peak regions in the spectra of both cohorts, indicative of essential life-supporting fatty acids. Notably, up to six lipid markers were identified in the first cohort, five of which displayed statistically significant differences (p < 0.05). However, the majority of these biomarkers in the first cohort did not show significant expression differences in the groups of embryos assigned to the second cohort. An exception was noted in the case of the Principal Lipid Component (PLC) marker, which was significantly less prevalent in the arrested embryos than in those progressing to the blastocyst stage (p = 0.0016). Further data analysis based on AI-classification algorithms revealed up to 80% precision in predicting the development to the blastocyst stage solely based on their metabolic profiles at the 8-cell stage. Limitations, reasons for caution Magnetic Resonance Spectroscopy (MRS) shows promise for clinical and research use, requiring reliable signal detection from cellular components. While suitable for research, clinical deployment demands further safety assessments and protocol refinement. Additionally, larger sample sizes and data on post-MRS implantation and pregnancy rates are needed to validate AI-classification algorithms. Wider implications of the findings Micro-Magnetic Resonance Spectroscopy (Micro-MRS) holds potential as a reliable and non-invasive method for embryo screening prior to transfer. This innovative tool can shed light on the role of embryonic metabolites during development making Micro-MRS a key instrument in advancing both embryology research and human assisted reproduction techniques. Trial registration number not applicable

Design of a Permanent Magnet Electromagnetic Acoustic Transducer (EMAT) for In-Situ Inspection of Metals at high Temperatures

In this proposed research, a permanent magnet longitudinal wave Electromagnetic Acoustic Transducer (EMAT) was designed for In-Situ inspection of metals at high temperatures. A permanent magnet EMAT generates Lorentz force inside metals by the interaction of magnetic fields and eddy currents. As temperature rises, various factors can influence the intensity of the Lorentz force, making it difficult to produce and detect ultrasonic waves. Thus, it is essential to optimize the sensor at ambient temperature before attempting to generate a reliable signal at elevated temperatures. The challenge highlighted has been addressed by optimizing the intensity of static field by varying the parameter like spacing between magnets that are being used. In order to minimize the effects of elevated temperatures, the sensor's stability has been enhanced by the implementation of efficient insulating measures. Furthermore, Finite Element (FE) simulations were conducted under on aluminium sample. These simulation results were then compared to experimental data to validate the efficiency of the designed sensor. Keywords: Permanent magnet, EMAT, Lorentz force, ultrasonic waves, high temperature, static magnetic field intensity.

Synergizing quantitative finance models and market microstructure analysis for enhanced algorithmic trading strategies

In today’s complex financial markets, “Algorithmic Trading” has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions.

Automated System for Attomolar-Level Detection of MiRNA as a Biomarker for Influenza A Virus.

We have developed an automated sensing system for the repeated detection of a specific microRNA (miRNA) of the influenza A (H1N1) virus. In this work, magnetic particles functionalized with DNAs, target miRNAs, and alkaline phosphate (ALP) enzymes formed sandwich structures. These particles were trapped on nickel (Ni) patterns of our sensor chip by an external magnetic field. Then, additional electrical signals from electrochemical markers generated by ALP enzymes were measured using the sensor, enabling the highly sensitive detection of target miRNA. The magnetic particles used on the sensor were easily removed by applying the opposite direction of external magnetic fields, which allowed us to repeat sensing measurements. As a proof of concept, we demonstrated the detection of miRNA-1254, one of the biomarkers for the H1N1 virus, with a high sensitivity down to 1 aM in real time. Moreover, our sensor could selectively detect the target from other miRNA samples. Importantly, our sensor chip showed reliable electrical signals even after six repeated miRNA sensing measurements. Furthermore, we achieved technical advances to utilize our sensor platform as part of an automated sensing system. In this regard, our reusable sensing platform could be utilized for versatile applications in the field of miRNA detection and basic research.