Signal Analysis Research Articles

Ultrahigh field diffusion magnetic resonance imaging uncovers intriguing microstructural changes in the adult zebrafish brain caused by Toll-like receptor 2 genomic deletion.

Toll-like receptor 2 (TLR2) belongs to the TLR protein family that plays an important role in the immune and inflammation response system. While TLR2 is predominantly expressed in immune cells, its expression has also been detected in the brain, specifically in microglia and astrocytes. Recent studies indicate that genomic deletion of TLR2 can result in impaired neurobehavioural function. It is currently not clear if the genomic deletion of TLR2 leads to any alterations in the microstructural features of the brain. In the current study, we noninvasively assess microstructural changes in the brain of TLR2-deficient (tlr2-/-) zebrafish using state-of-the art magnetic resonance imaging (MRI) methods at ultrahigh magnetic field strength (17.6 T). A significant increase in cortical thickness and an overall trend towards increased brain volumes were observed in young tlr2-/- zebrafish. An elevated T2 relaxation time and significantly reduced apparent diffusion coefficient (ADC) unveil brain-wide microstructural alterations, potentially indicative of cytotoxic oedema and astrogliosis in the tlr2-/- zebrafish. Multicomponent analysis of the ADC diffusivity signal by the phasor approach shows an increase in the slow ADC component associated with restricted diffusion. Diffusion tensor imaging and diffusion kurtosis imaging analysis revealed diminished diffusivity and enhanced kurtosis in various white matter tracks in tlr2-/- compared with control zebrafish, identifying the microstructural underpinnings associated with compromised white matter integrity and axonal degeneration. Taken together, our findings demonstrate that the genomic deletion of TLR2 results in severe alterations to the microstructural features of the zebrafish brain. This study also highlights the potential of ultrahigh field diffusion MRI techniques in discerning exceptionally fine microstructural details within the small zebrafish brain, offering potential for investigating microstructural changes in zebrafish models of various brain diseases.

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Fluidization characteristics of loaded vibrating fluidized bed and dewatering and deashing effect of moist fine-grained coal

ABSTRACT The influence of a thermal vibrating fluidized bed on the dewatering and deashing effect of moist fine-grained coal was investigated by digital image processing and signal analysis methods. Comparative analysis results of the pressure signals of a loaded vibrating fluidized bed indicated that high or low gas velocity and strong or weak vibration conditions easily caused the formation of numerous irregular bubbles and contributed to violent fluctuations in the bed. An interaction between gas velocity and vibration energy was found, with vibration energy being the dominant factor influencing the bubble behavior at low gas velocities. Moreover, at high gas velocities, the advantages arising from vibrations were weakened. Under the condition of f = 20 Hz, A = 2 mm, v = 18 cm⋅s−1, the contact efficiency between the gas – solid two phases was higher, and the pressure fluctuation of the bed became more stable. Under this condition, the ash content of clean coal and the moisture removal rate were 11.76% and 83.54%, respectively. Finally, comparison and analysis of the influence of different parameters on the calorific value of clean coal revealed that the influence of each parameter on heat generation of the selected product was found to be in the following order: f > A > V > T.

Intraoperative electroencephalogram patterns as predictors of postoperative delirium in older patients: a systematic review and meta-analysis

BackgroundPostoperative delirium (POD) significantly affects patient outcomes after surgery, leading to increased morbidity, extended hospital stays, and potential long-term cognitive decline. This study assessed the predictive value of intraoperative electroencephalography (EEG) patterns for POD in adults.MethodsThis systematic review and meta-analysis followed the PRISMA and Cochrane Handbook guidelines. A thorough literature search was conducted using PubMed, Medline, and CENTRAL databases focusing on intraoperative native EEG signal analysis in adult patients. The primary outcome was the relationship between the burst suppression EEG pattern and POD development.ResultsFrom the initial 435 articles identified, 19 studies with a total of 7,229 patients were included in the systematic review, with 10 included in the meta-analysis (3,705 patients). In patients exhibiting burst suppression, the POD incidence was 22.1% vs. 13.4% in those without this EEG pattern (p=0.015). Furthermore, an extended burst suppression duration associated with a higher likelihood of POD occurrence (p = 0.016). Interestingly, the burst suppression ratio showed no significant association with POD.ConclusionsThis study revealed a 41% increase in the relative risk of developing POD in cases where a burst suppression pattern was present. These results underscore the clinical relevance of intraoperative EEG monitoring in predicting POD in older patients, suggesting its potential role in preventive strategies.Systematic Review RegistrationThis study was registered on International Platform for Registered Protocols for Systematic Reviews and Meta-Analyses: INPLASY202420001, https://doi.org/10.37766/inplasy2024.2.0001.

Genomic and transcriptome analyses reveal potential contributors to erucic acid biosynthesis in seeds of rapeseed (Brassica napus).

Through comprehensive genomic and transcriptomic analyses, we identified a set of 23 genes that act up- or downstream of erucic acid content (EAC) production in rapeseed seeds. We selected example genes to showcase the distribution of single nucleotide polymorphisms, haplotypes associated with EAC phenotypes, and the creation of molecular markers differentiating low EAC and high EAC genotypes. Erucic acid content (EAC) is a crucial trait in rapeseed, with low LEAC oil recognized for its health benefits and high EA oil holding industrial value. Despite its significance, the genomic consequences of intensive LEAC-cultivar selection and the genetic basis underlying EA regulation remain largely unexplored. To address this knowledge gap, we conducted selective signal analyses, genome-wide association studies (GWAS), and transcriptome analyses. Our investigation unveiled the genetic footprints resulting from LEAC selection in germplasm populations, drawing attention to specific loci that contribute to enriching diversity. By integrating GWAS and transcriptome analyses, we identified a set of 23 genes that play a significant role in determining EAC in seeds or are downstream consequences of EA-level alterations. These genes have emerged as promising candidates for elucidating the potential mechanisms governing EAC in rapeseed. To exemplify the findings, we selected specific genes to demonstrate the distribution of single nucleotide polymorphisms and haplotypes associated with different EAC phenotypes. Additionally, we showcased to develop molecular markers distinguishing between LEAC and high EAC genotypes.

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

In Vivo Bioluminescence Imaging Reveals Differences in Leishmania infantum Parasite Killing Kinetics by Antileishmanial Reference Drugs.

The bioluminescent Leishmania infantum BALB/c mouse model was used to evaluate the parasiticidal drug action kinetics of the reference drugs miltefosine, paromomycin, sodium stibogluconate, and liposomal amphotericin B. Infected mice were treated for 5 days starting from 7 days post-infection, and parasite burdens were monitored over time via bioluminescence imaging (BLI). Using nonlinear regression analyses of the BLI signal, the parasite elimination half-life (t1/2) in the liver, bone marrow, and whole body was determined and compared for the different treatment regimens. Significant differences in parasiticidal kinetics were recorded. A single intravenous dose of 0.5 mg/kg liposomal amphotericin B was the fastest acting with a t1/2 of less than 1 day. Intraperitoneal injection of paromomycin at 320 mg/kg for 5 days proved to be the slowest with a t1/2 of about 5 days in the liver and 16 days in the bone marrow. To conclude, evaluation of the cidal kinetics of the different antileishmanial reference drugs revealed striking differences in their parasite elimination half-lives. This BLI approach also enables an in-depth pharmacodynamic comparison between novel drug leads and may constitute an essential tool for the design of potential drug combinations.

A non-invasive method to monitor respiratory muscle effort during mechanical ventilation.

This study introduces a method to non-invasively and automatically quantify respiratory muscle effort (Pmus) during mechanical ventilation (MV). The methodology hinges on numerically solving the respiratory system's equation of motion, utilizing measurements of airway pressure (Paw) and airflow (Faw). To evaluate the technique's effectiveness, Pmus was correlated with expected physiological responses. In volume-control (VC) mode, where tidal volume (VT) is pre-determined, Pmus is expected to be linked to Paw fluctuations. In contrast, during pressure-control (PC) mode, where Paw is held constant, Pmus should correlate with VT variations. The study utilized data from 250 patients on invasive MV. The data included detailed recordings of Paw and Faw, sampled at 31.25Hz and saved in 131.1-second epochs, each covering 34 to 41 breaths. The algorithm identified 51,268 epochs containing breaths on either VC or PC mode exclusively. In these epochs, Pmus and its pressure-time product (PmusPTP) were computed and correlated with Paw's pressure-time product (PawPTP) and VT, respectively. There was a strong correlation of PmusPTP with PawPTP in VC mode (R² = 0.91 [0.76, 0.96]; n = 17,648 epochs) and with VT in PC mode (R² = 0.88 [0.74, 0.94]; n = 33,620 epochs), confirming the hypothesis. As expected, negligible correlations were observed between PmusPTP and VT in VC mode (R² = 0.03) and between PmusPTP and PawPTP in PC mode (R² = 0.06). The study supports the feasibility of assessing respiratory effort during MV non-invasively through airway signal analysis. Further research is warranted to validate this method and investigate its clinical applications.

Correlation Analysis of Multi-Scale Ictal EEG Signals in Juvenile Myoclonic Epilepsy

Correlation Analysis of Multi-Scale Ictal EEG Signals in Juvenile Myoclonic Epilepsy

Detection of Gate Valve Leaks through the Analysis Fractal Characteristics of Acoustic Signal

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes.

[Detection of EWSR1 gene rearrangement by fluorescence in situ hybridization in bone and soft tissue tumors: clinical application evaluation and atypical signal analysis].

Objective: To investigate the clinical application of EWSR1 gene rearrangement by fluorescence in situ hybridization (FISH) in bone and soft tissue tumors and to analyze the cases with atypical signal pattern. Methods: The cases detected for EWSR1 gene rearrangement by FISH in Beijing Jishuitan Hospital, Capital Medical University from 2014 to 2021 were collected, and the value of detecting EWSR1 gene rearrangement for diagnosing bone and soft tissue tumors was analyzed. The cases with atypical positive signals were further analyzed by next generation sequencing (NGS). Results: FISH using EWSR1 break-apart probe kit was successfully performed in 97% (205/211) of cases, 6 cases failed. Four of the 6 failures were due to improper decalcification, 1 case due to signal overlap caused by thick slices, and 1 case due to signal amplification and disorder. EWSR1 gene rearrangements were positive in 122 cases (122/205, 59%), atypical positive signal in 8 cases (8/205, 4%), and negative in 75 cases (75/205, 37%). In cases testing positive, the percentage of positive cells ranged from 34% to 98%, with 120 cases (120/122, 98%) showing a positive cell percentage greater than 50%. Among the 205 successfully tested cases, 156 cases were histologically diagnosed as Ewing's sarcoma, of which 110 were positive (110/156, 71%), 7 were atypical positive (7/156, 4%), and 39 were negative (39/156, 25%). Nine cases were histologically diagnosed as clear cell sarcoma of soft tissue, of which 6 were positive (6/9), 1 was atypical positive (1/9), and 2 were negative (2/9). Five cases were histologically diagnosed as extraskeletal myxoid chondrosarcoma, of which 2 were positive (2/5) and 3 were negative (3/5). Three cases were histologically diagnosed as angiomatoid fibrous histiocytoma, of which 2 were positive (2/3) and 1 was negative (1/3). Two cases were histologically diagnosed as myoepithelioma of soft tissue, of which 1 was positive (1/2) and 1 was negative (1/2). One case was histologically diagnosed as olfactory neuroblastoma with a positive result. The 29 other tumor cases including osteosarcoma, synovial sarcoma, and malignant melanoma and others were all negative. Basing on histology as the standard for diagnosis and considering atypical positive cases as negative, comparing with the 29 cases of other tumors as control group, the sensitivity for diagnosing Ewing's sarcoma through the detection of EWSR1 gene rearrangement was 71%, and the specificity was 100%; the sensitivity for diagnosing clear cell sarcoma of soft tissue was 67%, and the specificity was 100%; the sensitivity for diagnosing extraskeletal myxoid chondrosarcoma was 40%, and the specificity was 100%; the sensitivity for diagnosing angiomatoid fibrous histiocytoma was 67%, and the specificity was 100%; the sensitivity for diagnosing myoepithelioma of soft tissue was 50%, and the specificity was 100%; the sensitivity for diagnosing olfactory neuroblastoma was 100%, and the specificity was 100%. Four of 8 cases with atypical positive signals analyzed by NGS showed EWSR1 rearrangement, including EWSR1::FLI1 in one case of Ewing sarcoma, EWSR1::NFATC2 in one case of EWSR1::NFATC2-rearranged sarcoma, EWSR1::ATF1 in one case of clear cell sarcoma of soft tissue and EWSR1::NR4A3 in one case of extraskeletal myxoid chondrosarcoma. Conclusions: Detection of EWSR1 rearrangement by FISH is of utmost significance in the diagnosis of bone and soft tissue tumors. Cases with atypical positive signals should be further scrutinized, correlating with their histomorphology and verifying by NGS if necessary.

Comprehensive Signal Analysis: Unraveling Mean Frequency, Power, and Bandwidth

Objective: The power spectral density (PSD) serves as a fundamental tool in signal analysis, revealing valuable insights into the distribution of power across frequencies. This paper explores the concept of PSD, which emerges from the Fourier transform of a signal’s autocorrelation function. By examining power distribution, PSD provides a comprehensive understanding of a signal’s spectral characteristics. Methods: We generate a chirp signal with specific parameters, including initial and final frequencies, and introduce white Gaussian noise. The combination of these signals forms a composite signal, enabling the calculation of its PSD. Results: Visualizing the PSD allows for discernment of the frequency distribution, with the median frequency providing insights into the central tendency of the distribution. Conclusion: Furthermore, the customization of plots enhances the visual representation of data, tailoring it to the requirements of the specific application. Adjustments to color schemes, line styles, and annotations improve clarity and aid in conveying complex information effectively.

An end-cloud collaboration approach for online state-of-health estimation of lithium-ion batteries based on multi-feature and transformer

State-of-Health (SOH) estimation of lithium-ion batteries is critical for reliable and efficient energy storage and usage. Traditional SOH estimation approaches either compromise on real-time performance or on estimation accuracy. This paper introduces an end-cloud collaboration approach that synergistically combines a cloud-based deep learning model with an end-based empirical model to achieve both high-accuracy and real-time SOH estimation. Utilizing the Transformer architecture, the cloud-side model employs multiple features extracted through signal analysis technology. On the other hand, the end-side employs a fast, iterative double exponential empirical model that provides real-time estimates. Two methods of end-cloud collaborative updating are presented, both leveraging Kalman Filter and Unscented Kalman Filter (UKF) to integrate and iteratively update the models. Evaluation shows that the cloud-side model achieves a Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of around 0.8 %, while the end-cloud collaboration system maintains these metrics at 1 %, successfully capturing nonlinear capacity rebound phenomena. The work contributes to the domain by offering an approach that balances computational load and latency, making it suitable for online applications. Under the Cyber Hierarchy and Interactional Network (CHAIN) framework, this end-cloud collaboration architecture holds promise for high-accuracy, real-time health management of lithium-ion batteries across their full lifecycle.

Circumferential slots based decoupled wideband MIMO antenna for 5G and sub-6 GHz applications

This paper investigates a small size 2 × 2 wideband multiple input multiple output (MIMO) antenna covering frequency band of 2.61 to 5.52 GHz. The antenna structure consists of elliptical ring patch with multiple circumferential slots. The edge-to-edge separation between the patches are kept 3.6 mm with volumetric dimensions of 30 × 46 × 1.6 mm3. The narrow slits are etched in the tapered ground plane to obtain the wideband characteristics. The electromagnetic coupling is improved more than 16 dB by employing the neutralization technique. The measured average gain and radiation efficiency of the proposed antenna are above 2.61dBi and 83.54%, respectively across the operating band. In addition to that, the important MIMO diversity parameters are investigated. The group delay and signal analysis in different orientations are also studied to ensure the quality performance of the antenna. The proposed MIMO antenna is simulated using CST microwave studio and further validated with the measurements. In addition to that, the parallel and orthogonal configuration of 4 × 4 MIMO antennas are analyzed by extending the proposed 2 × 2 MIMO design. The 4-port MIMO antennas exhibit the operating frequency bands varying from 2.80 to 5.58 GHz and 3.4 to 5.52 GHz, respectively for parallel and orthogonal MIMO configurations. The diversity parameters of 4-port MIMO antennas are calculated and compared with the measured results to verify its practical applications. Finaly, the SAR analysis of the antenna is presented at 3.08 and 5.20 GHz for 1/10 g tissues and are found 0.111/0.106 and 0.079/0.063 W K−1g−1, respectively. The proposed MIMO antenna designs are suitable for modern high-speed communication for 5G and sub-6 GHz frequency bands.

Long-term exposure to ambient air pollution and measures of central hemodynamics and arterial stiffness among multiethnic Chicago residents

ObjectivesTo examine whether long-term air pollution exposure is associated with central hemodynamic and brachial artery stiffness parameters.MethodsWe assessed central hemodynamic parameters including central blood pressure, cardiac parameters, systemic vascular compliance and resistance, and brachial artery stiffness measures [including brachial artery distensibility (BAD), compliance (BAC), and resistance (BAR)] using waveform analysis of the arterial pressure signals obtained from a standard cuff sphygmomanometer (DynaPulse2000A, San Diego, CA). The long-term exposures to particles with an aerodynamic diameter < 2.5 μm (PM2.5) and nitrogen dioxide (NO2) for the 3-year periods prior to enrollment were estimated at residential addresses using fine-scale intra-urban spatiotemporal models. Linear mixed models adjusted for potential confounders were used to examine associations between air pollution exposures and health outcomes.ResultsThe cross-sectional study included 2,387 Chicago residents (76% African Americans) enrolled in the ChicagO Multiethnic Prevention And Surveillance Study (COMPASS) during 2013–2018 with validated address information, PM2.5 or NO2, key covariates, and hemodynamics measurements. We observed long-term concentrations of PM2.5 and NO2 to be positively associated with central systolic, pulse pressure and BAR, and negatively associated with BAD, and BAC after adjusting for relevant covariates. A 1-µg/m3 increment in preceding 3-year exposures to PM2.5 was associated with 1.8 mmHg higher central systolic (95% CI: 0.98, 4.16), 1.0 mmHg higher central pulse pressure (95% CI: 0.42, 2.87), a 0.56%mmHg lower BAD (95% CI: -0.81, -0.30), and a 0.009 mL/mmHg lower BAC (95% CI: -0.01, -0.01).ConclusionThis population-based study provides evidence that long-term exposures to PM2.5 and NO2 is related to central BP and arterial stiffness parameters, especially among African Americans.

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual’s body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data.

Evaluation of stress-loaded ferromagnetic structures using the magnetic recording method and extended signal analysis

This paper presents a new method of analysis of results obtained using the Magnetic Recording Method (MRM), which was applied to inspect tensile stress-loaded ferromagnetic samples. The MRM method comprised a two-step procedure. In the first step, a strictly defined magnetic pattern is recorded on the samples using an array of permanent magnets, and then the original residual magnetization is measured. Next, the samples are subjected to static stress. In the second step, the residual magnetization measurement is repeated. Comparative analysis of the measurement results allows for an accurate and unambiguous assessment of applied stress in steel components. Moreover, the magnetization was measured again after three years to investigate the effect of time on magnetization patterns. The study confirms that the method is stable and has a negligible time dependence.

In vitro analysis of FSHR internalization and signalling mediated by two different FSH preparations

<i>In vitro</i> analysis of FSHR internalization and signalling mediated by two different FSH preparations

Arduino-Based Readout Electronics for Nuclear and Particle Physics.

Open Hardware-based microcontrollers, especially the Arduino platform, have become a comparably easy-to-use tool for rapid prototyping and implementing creative solutions. Such devices in combination with dedicated front-end electronics can offer low-cost alternatives for student projects, slow control and independently operating small-scale instrumentation. The capabilities can be extended to data taking and signal analysis at mid-level rates. Two detector realizations are presented, which cover the readouts of proportional counter tubes and of scintillators or wavelength-shifting fibers with silicon photomultipliers (SiPMs). The SiPMTrigger realizes a small-scale design for coincidence readout of SiPMs as a trigger or veto detector. It consists of a custom mixed signal front-end board featuring signal amplification, discrimination and a coincidence unit for rates of up to 200 kHz. The nCatcher transforms an Arduino Nano to a proportional counter readout with pulse shape analysis: time over threshold measurement and a 10-bit analog-to-digital converter for pulse heights. The device is suitable for low-to-medium-rate environments up to 5 kHz, where a good signal-to-noise ratio is crucial. We showcase the monitoring of thermal neutrons. For data taking and slow control, a logger board is presented that features an SD card and GSM/LoRa interface.

Stochastic Electrochemical Measurement of a Biofouling Layer on Gold.

Adsorption of a biofouling layer on the surface of biosensors decreases the electrochemical activity and hence shortens the service life of biosensors, particularly implantable and wearable biosensors. Real-time quantification of the loss of activity is important for in situ assessment of performance while presenting an opportunity to compensate for the loss of activity and recalibrate the sensor to extend the service life. Here, we introduce an electrochemical noise measurement technique as a tool for the quantification of the formation of a biofouling layer on the surface of gold. The technique uniquely affords thermodynamic and kinetic information without applying an external bias (potential and/or current), hence allowing the system to be appraised in its innate state. The technique relies on the analysis of non-faradaic current and potential fluctuations that are intrinsically generated by the interaction of charged species at the electrode surface, i.e., gold. An analytical model is extended to explain the significance of parameters drawn from statistical analysis of the noise signal. This concept is then examined in buffered media in the presence of albumin, a common protein in the blood and a known source of a fouling layer in biological systems. Results indicate that the statistical analysis of the noise signal can quantify the loss of electrochemical activity, which is also corroborated by impedance spectroscopy as a complementary technique.