Sampling Rate Research Articles

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

DEVELOPMENT AND DATA ANALYSIS OF A ROBO-PEN FOR ALZHEIMER’S DISEASE DIAGNOSIS: PRELIMINARY RESULTS

Alzheimer’s Disease (AD) poses a significant challenge in contemporary medicine, necessitating early and accurate diagnostic methods to manage its progression effectively. This study explores the development and application of the Robo-pen, an innovative diagnostic tool designed to detect early signs of cognitive decline through detailed handwriting analysis. The Robo-pen, equipped with an MPU-9250 sensor, captures three-dimensional coordinates, velocity, and acceleration of handwriting movements, crucial for assessing spatial control, movement consistency, speed variations, and the ability to modulate movement speed and force–parameters often disrupted in cognitive impairments like AD. Participants included 20 patients diagnosed with AD and 18 healthy controls, matched in age and educational levels. Data collection involved tasks such as sentence rewriting, figure redrawing, and digit rewriting, processed using CoolTerm software at a sampling rate of 18 Hz. Descriptive statistics revealed that the AD group exhibited lower mean values for gyroscope and acceleration data, indicating slower and less variable movements compared to the control group. T-tests confirmed significant differences (p < 0.001) across all measured parameters between the AD and control groups. The results support the potential of the Robo-pen as a non-invasive, cost-effective diagnostic tool for early detection of AD. By capturing subtle neuromotor changes, the Robo-pen facilitates earlier diagnosis and timely intervention, potentially altering the disease trajectory and improving patient outcomes. This study marks a significant advancement in the early detection of AD, highlighting the Robo-pen’s promise as a transformative tool in neurodegenerative disease diagnosis and management.

MHz sampling rate measurements of N2(A3Σu+) population in a Ns pulse discharge in a heated plasma flow reactor

Abstract Absolute, time-resolved population of a metastable electronic state of molecular nitrogen, N2( A 3 Σ u + , v = 0 ), is measured in a heated plasma flow reactor excited by a ns pulse discharge burst, by tunable diode laser absorption spectroscopy using a distributed feedback laser. Nitrogen or NO–N2 gas mixture in the reactor are heated by a tube furnace maintained at T = 800–1000 K, at pressures of P = 50–300 Torr. A diffuse plasma in the flow channel made of quartz is generated using a repetitively pulsed, double dielectric barrier, ns discharge in a plane-to-plane geometry. N2( A 3 Σ u + ) molecules in the plasma are generated by electron impact excitation of N2 ( B 3 Π g ) and N2 ( C 3 Π u ) states, followed by their cascade quenching. The laser is tuned across an absorption line near 1028 nm in a N2 B 3 Π g , v ′ = 0 ← A 3 Σ u + , v ′ ′ = 0 band at 100 kHz–1 MHz. The laser output wavelength and the absorption signal are measured by an etalon and two high bandwidth detectors. At all operating conditions, the absorption signal is fully resolved in time. The data points are taken every 500 ns–5 μs, with the temporal uncertainty of 50–500 ns, respectively. The data are used to infer the line pressure broadening coefficient and its temperature dependence. This study demonstrates the feasibility of this approach for the time-resolved N2( A 3 Σ u + ) measurements in pulsed hypersonic flow facilities, behind strong shock waves and in hypervelocity expansion flows.

Design of a new scintillator-based fast ion loss detector on the HL-2A tokamak

A new scintillator-based fast ion loss detector (FILD) has been devised for the HL-2A tokamak, with the objective of discerning the temporal evolution of energy and pitch angle among lost fast ions. A code named FILD Simulation (FSC) has been developed to assist in the design of the detector head and the interpretation of experimental data. By optimising the geometric design, the resolutions of energy and pitch angle are optimised to 5 keV and 3°, respectively. A branched imaging fiber bundle is used to relay the fluorescence image on the scintillator to two distinct instruments. One of these is a high-speed camera, while the other is a silicon photomultiplier tube (SiPM) array with a sampling rate of up to 1 MSamples/s. This configuration enables the attainment of superior temporal and spatial resolution, as well as high photon sensitivity.

Barefoot vs shod walking and jogging on the electromyographic activity of the medial and lateral gastrocnemius

Gastrocnemius weakness is associated with Achilles tendinopathies and muscle strains, with the medial gastrocnemius (MG) more commonly injured than the lateral gastrocnemius (LG). Walking and jogging are common in daily activities and sports, and biomechanical differences between shod and barefoot exercise may influence MG and LG activation. Understanding these activation patterns could help optimize training programs for injury prevention and/or rehabilitation. The aim was to compare MG and LG electromyographic activity during walking and jogging, both shod and barefoot. Twenty-nine participants (25.28 ± 4.53 years, 171.31 ± 0.76 cm, 72.68 ± 6.36 kg) completed a warm-up followed by 1 min of walking (80–99 steps/min) and jogging (130–150 steps/min) in both conditions (barefoot and shod, random order). Electromyographic signals were recorded using wearable devices (mDurance Solutions S.L., Granada, Spain; 1024 Hz sampling rate). We measured the root-mean-square (RMS) amplitudes for an entire stride cycle and digitally filtered the signals. For analysis, we normalized electromyographic values to the average peak values obtained during two sprints. We analyzed differences with a repeated-measures analysis of variance. Significant effects of condition (barefoot-shod) and gastrocnemius (MG-LG) were observed (all p ≤ 0.023, ƞp2 = 0.17–0.39), with higher MG activation compared to LG in the barefoot conditions (p = 0.004–0.027, d = 0.72–0.83), and nonsignificant differences between muscles in the shod conditions (p > 0.05). Shod exercise compared to barefoot resulted in lower MG activation (p = 0.001–0.003, d = 0.62–0.63) and non-significant differences in LG activation. These results indicate that barefoot walking and jogging increase MG activation compared to shod conditions, with no differences in LG activation. Additionally, footwear reduces differences between MG and LG.

Afterslip and Creep in the Rate‐Dependent Framework: Joint Inversion of Borehole Strain and GNSS Displacements for the Mw 7.1 Ridgecrest Earthquake

AbstractThe elusive transition toward afterslip following an earthquake is challenging to capture with typical data resolution limits. A dense geodetic network recorded the Mw 7.1 Ridgecrest earthquake, including 16 Global Navigation Satellite System (GNSS) stations and 3 borehole strainmeters (BSM). The sub‐nanostrain precision and sub‐second sampling rate of BSMs bridges a gap between conventional seismologic and geodetic methods, exemplified by atypical postseismic shear strain reversals observed at nearfield (<2 km) station B921 that remain unexplained. We jointly invert GNSS displacements and BSM strains for coseismic and postseismic slip spanning hours to months over 7 independent periods. Cosiesmically, our model resolves the largest slip magnitudes of up to 6.6 m on the mainshock rupture plane, with similar patterns to other inferred slip distributions. The foreshock fault appears to slip coincidently with mainshock, revealing potential asperities activated during the preceding Mw 6.4 event. Postseismically, the best‐fitting models adhere to mechanical rate‐and‐state expectations of logarithmically decaying slip adjacent to the coseismic rupture terminus, and where deep rheologic conditions favor creep. Most spatial variation occurs in the early postseismic timeframe (<1–2 weeks), with evidence for regional rheologic control and static stress dependence. Triggered creep on the neighboring Garlock Fault unexpectedly persists for >178 days—further highlighting the importance of fault networks in postseismic stress redistribution, critical to assessing future hazard.

Effect of WHU/GFZ/CODE satellite attitude quaternion products on the GNSS kinematic PPP during the eclipse season

To ensure high-precision Global Navigation Satellite System (GNSS) applications, many analysis centers (ACs) have begun to provide satellite attitude products, which affect the modeling of phase variations and phase wind-up. Despite several studies having demonstrated the importance of providing attitude products, particularly eclipsing products, the performance of satellite attitude models for different ACs has not been further investigated. This study compared the satellite attitude models provided by Wuhan university (WHU), German Research Centre of Geosciences (GFZ), and Center for Orbit Determination in Europe (CODE), and investigated the impact of satellite attitude products on high-rate GNSS data processing (i.e., 1HZ). The satellite attitudes of most satellite blocks were consistent well with each other, except for the BDS-2 inclined geostationary orbit (IGSO), BDS-3 medium earth orbit (MEO), GPS IIIA, and GLONASS-M satellites. The differences in the yaw angle between the different ACs ranged as 10–180°. Compared to the good consistency of the wum and com products, gbm exhibited significant differences from the former for most constellations. Following the application of the satellite attitude products, the average 3D RMS value of the single-kinematic PPP was improved by 11% and 10% for the 30-s and 1-s sampling rate observations, respectively. Thus, these eclipsing satellites with inaccurate attitude models can be deleted while integrating more satellites into the GNSS data processing.

Deactivated fused silica tubing: A solution for flexible time weighted averaging needle trap sampling

In time-weighted averaging (TWA) with needle trap extraction (NTE), the control of the sampling rate is critical for accurate analysis. By adjusting the diffusion length and cross-sectional area, the sampling rate can be modified in accordance with Fick's first law of diffusion. In this study, deactivated fused silica tubing (DFST) of varying lengths was used to fine-tune these parameters, allowing for precise control of the sampling rate in TWA-NTE devices.The fabricated devices were used to extract benzene, toluene, ethylbenzene, o-xylene (BTEX), and selected alkanes as model compounds. Experimental sampling rates were obtained using a standard gas flow generating system and compared to theoretical values, showing statistical similarity. The devices were tested in various real-world environments, including a parking lot, garages, and a box containing a burning candle, and their practical applicability was confirmed. The use of DFST effectively controlled both diffusion length and cross-sectional area, thereby enhancing sampling performance.The results of this study demonstrate that DFST serves as a versatile and adjustable attachment for needle trap devices (NTD), significantly broadening the range of NTD applications in terms of concentration and sampling time. This approach not only reduces analysis costs but also improves adherence to Fick's law by minimizing the influence of other mass transfer mechanisms. Consequently, more consistent and predictable extraction behavior was observed, particularly for higher molecular weight species, strengthening the overall applicability of the TWA-NTE method.

A super-resolution algorithm of Ghost Imaging using CNN with Grouped orthonormalization algorithm Constraint

Ghost imaging (GI) is capable of reconstructing images under low-light conditions by single-pixel measurements. However, improving image resolution often requires extensive single-pixel sampling, limiting practical applications. Here we propose a super-resolution algorithm of GI using Convolutional neural network with Grouped orthonormalization algorithm Constraint (GICGC), which aims to reconstruct images at super-resolution with strong local regularities and self-similarity. The proposed algorithm, a versatile approach, has been demonstrated to outperform several other widely used GI algorithms in terms of spatial resolution and sampling rate. Our findings are supported by rigorous benchmark tests and experimental validations in challenging environments, including multimode fibers. The imaging experimental results demonstrate that GICGC achieves superior performance in reconstructing image linewidth and contrast, effectively doubling resolution capability by approximately 2.1 times, indicating significant potential in biomedical imaging and other fields. We believe this study represents a novel breakthrough in universal super-resolution ghost imaging and paves the way for its practical applications.

Дослідження впливу природної та штучної пасивації на швидкість корозії магнієвого сплаву для хірургічних імплантатів

Magnesium-based alloys are widely used materials for surgical implants. Considerable efforts of researchers are applied to the development of new magnesium alloys and methods of their surface treatment in order to protect against biocorrosion or to regulate its speed. The idea of the work is to determine the potential of known magnesium alloys as materials for the production of implants. Industrial magnesium based alloys were analyzed from the point of view of suitability for use for surgical biodissolvable implants. It is shown that the ML10 alloy is the most suitable as it does not contain toxic components, namely aluminum, nickel and cadmium. Samples for corrosion tests were made from a billet of ML10 alloy after processing by plastic deformation by pressing through a calibrated die at a temperature of 375 ± 20 °С. Together with the ML10 alloy, comparative studies of the ML5 alloy were conducted. Corrosion tests of ML5 and ML10 alloys without a coating, as well as of the ML10 alloy with a coating applied by the micro-arc oxidation (MAO) method for 30 seconds, were carried out using the volumetric method. The samples were kept in a 3% NaCl solution at a temperature of 38 ± 2 °C. The corrosion rate of the ML5 alloy was at a constant level during the experiment. No significant ability of the ML5 alloy to self-passivate was found. The ML10 alloy turned out to be capable of significant self-passivation under the influence of a corrosive environment. During the experiment, the corrosion rate of the ML10 alloy sample decreased approximately ten times. It was also established that the corrosion rate of ML10 alloy samples with MAO coating was approximately three times lower than that of samples of the same alloy without coating. Preliminary information was obtained on the reduction of the corrosion rate of the ML10 alloy after plastic deformation. Further research can be aimed at confirming this phenomenon, determining its causes, as well as establishing quantitative indicators of the influence of the degree of plastic deformation on the corrosion rate of the ML10 alloy. Keywords: surgical implants, magnesium alloys, corrosion, self-passivation, coating.

X-ray ghost imaging with a specially developed beam splitter.

X-ray ghost imaging with a crystal beam splitter has advantages in highly efficient imaging due to the simultaneous acquisition of signals from both the object beam and reference beam. However, beam splitting with a large field of view, uniform distribution and high correlation has been a great challenge up to now. Therefore, a dedicated beam splitter has been developed by optimizing the optical layout of a synchrotron radiation beamline and the fabrication process of a Laue crystal. A large field of view, consistent size, uniform intensity distribution and high correlation were obtained simultaneously for the two split beams. Modulated by a piece of copper foam upstream of the splitter, a correlation of 92% between the speckle fields of the object and reference beam and a Glauber function of 1.25 were achieved. Taking advantage of synthetic aperture X-ray ghost imaging (SAXGI), a circuit board of size 880 × 330 pixels was successfully imaged with high fidelity. In addition, even though 16 measurements corresponding to a sampling rate of 1% in SAXGI were used for image reconstruction, the skeleton structure of the circuit board can still be determined. In conclusion, the specially developed beam splitter is applicable for the efficient implementation of X-ray ghost imaging.

On the Feasibility and Benefits of Extensive Evaluation

Benchmark and system parameters often have a significant impact on performance evaluation, which raises a long-lasting question about which settings we should use. This paper studies the feasibility and benefits of extensive evaluation. A full extensive evaluation, which tests all possible settings, is usually too expensive. This work investigates whether it is possible to sample a subset of the settings and, upon them, generate observations that match those from a full extensive evaluation. Towards this goal, we have explored the incremental sampling approach, which starts by measuring a small subset of random settings, builds a prediction model on these samples using the popular ANOVA approach, adds more samples if the model is not accurate enough, and terminates otherwise. To summarize our findings: 1) Enhancing a research prototype to support extensive evaluation mostly involves changing hard-coded configurations, which does not take much effort. 2) Some systems are highly predictable, which means that they can achieve accurate predictions with a low sampling rate, but some systems are less predictable. 3) We have not found a method that can consistently outperform random sampling + ANOVA. Based on these findings, we provide recommendations to improve artifact predictability and strategies for selecting parameter values during evaluation.

Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.

Dead-reckoning facilitates determination of activity and habitat use: a case study with European badgers (Meles meles)

BackgroundStudies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited.MethodsCollars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type.ResultsNightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively).Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances.ConclusionsThis study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

Enhancing the performance of DAS using a dual-wavelength UWFBG array with alternating arrangement

We propose a novel distributed acoustic sensor (DAS) based on a dual-wavelength ultra-weak fiber Bragg grating (UWFBG) array. The UWFBGs in the array have two wavelengths that are arranged alternately. Two double-pulses with different wavelengths and staggered launching times are used for sensing in the UWFBG array. This approach successfully overcomes the trade-off between spatial resolution and sensing distance in the traditional UWFBG-based DAS and effectively doubles the maximum achievable sampling rate constrained by the sensing distance. The crosstalk of different wavelengths is avoided by time division multiplexing. Comprehensive analyses and simulations are conducted to validate the performance enhancements, compared to the traditional method. In our proof-of-concept experiments, employing a 2 km alternating arranged dual-wavelength UWFBG array, we achieve a spatial resolution enhancement from 10 m to 5 m, expand the dynamic strain measurement range from 34.2 rad to 68.2 rad, and increase the frequency response from 9 kHz to 19 kHz.

Semantic ghost imaging based on semantic coding

To address the challenge of reconstructing high-quality images under low sampling during the Ghost Imaging (GI) procedure, we propose a semantic GI method based on semantic encoding. Through training, the obtained continuous weights of the convolution kernels are served as the speckle patterns. A processing module and a Recurrent Neural Network (RNN) are then employed to decode and reconstruct the images. Both the speckle patterns and their corresponding bucket values are optimized, so that the semantic information of the target object can be better extracted. With different gated recurrent units (GRU) layers for the target objects in different datasets, the feasibility of the proposed GI method is validated by the numerical simulations and experiments on the simple target objects (the handwriting digits “0” to “9”) and more complex target objects (“NUPT”). The results show that the target objects (the handwriting digits) can be reconstructed with higher quality even at a lower sampling rate of 1.28%. Additionally, the proposed method has applicability for more complex objects (such as “NUPT”) in real applications. In comparison with those results by using traditional ghost imaging (TGI), deep convolution auto-encoder network (DCAN), and RNN-based GI (GI-RNN), the proposed GI method shows a better performance in terms of the quality of reconstructed images and the training time, that is, the proposed method can have both good reconstructed image quality and less training time. By introducing the concept of semantic communication into GI, the proposed method provides a new idea for the model-based GI.

Development of Anemometer Based on Inertial Sensor.

The current article elucidates a study centered on the development of an anemometer leveraging an inertial sensor for wind speed measurement in the northeast region of Brazil, focusing on renewable energy generation. The study encompassed a series of experiments aimed at calibrating the anemometer, analyzing the noise generated by the inertial sensor, and scrutinizing the data acquired during wind speed measurement. The calibration process unfolded in three stages: initial noise analysis, subsequent inertial data analysis, and the derivation of calibration curves. The first two stages involved experiments conducted at an average sampling rate of 10 Hz. Simultaneously, the third stage incorporated data collected over a 1 h duration while maintaining the same sampling rate. The outcomes underscore the suitability of the anemometer based on an inertial sensor for wind energy systems and diverse applications. While the wind readings from the prototype exhibit considerable fluctuations, a three-length moving average filter is applied to the prototype's output to mitigate these fluctuations. The calibration surface was established using observational data, and the resultant surface is detailed. Data analysis assumes paramount significance in wind speed measurement, and the K-NN algorithm demonstrated superior efficacy in estimating the correspondence between measured and control data.

Guanidine Derivatives Leverage the Antibacterial Performance of Bio-Based Polyamide PA56 Fibres

Bacterial damage has significantly impacted humanity, prompting the control of harmful microorganisms and infectious diseases. In this study, antibacterial bio-based PA56 fibres were prepared with high-speed spinning using ethylene-methyl acrylate-glycidyl methacrylate terpolymer (EMA) as the compatibiliser and polypentamethylene guanidine sulphate (PPGS) as the antibacterial agent. The effects of PPGS content on the properties of PA56 draw-textured yarns (DTYs) were investigated. The compatibility between PPGS and PA greatly improved with EMA incorporation. Compared with PA56 fibres, the elongation at break of the sample containing 2.0 wt% EMA and PPGS increased by 25.93%. The inhibition rates of the fibres with 1.0 wt% PPGS against Escherichia coli and Staphylococcus aureus reached over 99.99%. Samples were easily coloured with dyes, exhibiting good colour fastness, regardless of the EMA content. However, the antibacterial performances of dyed DTYs decreased to varying degrees. the inhibition rates of samples of 0.5wt% addition of PPGS against E. coli were reduced from 99.99% to 28.50% and 25.36% after dyeing with Acid Blue 80 and Dispersible Blue 2BLN, respectively. The EMA-modified fibres exhibited the best antibacterial activity after dyeing with neutral gray 2BL. These findings are expected to promote the wider use of biobased PA56 in practical applications that require antibacterial performance and to guide the dyeing process of antimicrobial fibres.

Dynamic distributed Brillouin sensing based on pump frequency-agile quadrupling modulation

We propose a dynamic distributed Brillouin sensing based on a frequency-agile quadrupling method. A common low-bandwidth direct digital frequency synthesizer (DDS) is employed to generate a few hundred MHz of frequency-agile pump pulses for Brillouin gain spectrum (BGS) scanning, and polarization multiplexing technology is utilized to further decrease the bandwidth requirement for the frequency-agile microwave signal to one-quarter. This method has low requirements for electronic devices and significantly reduces costs in systems. Static and dynamic measurement capabilities of 2 km fiber were demonstrated experimentally, the vibration frequencies of 183 Hz were measured with a sampling rate of 400 Sa/s.

AI-enhanced backpack with double frequency-up conversion vibration energy converter for motion recognition and extended battery life

With growing interest in physical activities, wearable device sales have been increasing each year due to their help in tracking movement. However, keeping these devices running for a long time is still a key challenge. This paper presents an AI-enhanced backpack equipped with a hybrid vibration energy converter that integrates piezoelectric (PE) and electromagnetic (EM) technologies, featuring a double frequency-up conversion (FUC) mechanism. The PE unit enables efficient motion recognition, while the EM unit extends battery life via kinetic energy harvesting. The double FUC mechanism boosts human motion signal strength and resolution by converting sub-Hertz frequencies to hundreds of Hertz. This makes the device particularly effective in environments with performance constraints and interference. By adopting deep learning techniques, the system maintains a high motion recognition accuracy of 97.33 % even under low data sampling rates and significant noise interference. The EM unit effectively harvests kinetic energy, generating an average output power of 4.5 mW during activities such as running at 4.5 Hz, despite of the device’s compact size. Additionally, a power management circuit with human motion-stimulated switch optimizes battery life by managing the system’s sleep and active states. This innovative energy management approach ensures that during human activity, the system conserves energy by remaining in sleep state for at least 30 % of the time, significantly extending battery life and enhancing the backpack’s suitability for outdoor applications.