Sampling Rate Research Articles

Inhibiting stress corrosion cracking of a prefabricated surface‐defective magnesium alloy thanks to biological organic components

AbstractBiomedical magnesium (Mg) alloys remain a challenge for their mainstream application, because the combination of bio‐mechanical stress and corrosive physiological environment leading to stress corrosion cracking (SCC). It is crucial to avoid the sudden brittle fracture of Mg alloys in vivo for predicting their service duration. However, the key factors, such as surface or physiological environment features, determining the origination or propagation of SCC behavior are still unclear. In the present study, a prefabricated surface defects coating was prepared by the phytic acid (PA, C6H18O24P6) conversion treatment. Four mimicking physiological media were used, ranging from simple to equivalent (Dulbecco's Modified Eagle Medium and Protein [DMEM+Pro]). The results showed that the PA film with numerous micro‐cracks provided limited protective ability in synthetic biological media. A striking finding was determined that although initial high corrosion rate of samples in DMEM+Pro led to an increased SCC nucleation, significant ductile fracture with elongation to failure (14.86%) was observed. Combined with the fracture features, the adsorption or deposition of biological composition into the tunnel of SCC cracks significantly inhibited the hydrogen embrittlement (HE) behavior. These results indicate that preventing the propagation of SCC crack by biological composition, rather than nucleation, plays a key role in avoiding the sudden fracture of Mg alloys. It provides a novel perspective to determine the non‐brittle fracture of Mg alloys for biomedical applications.

Piezoelectric Sensors as Energy Harvesters for Ultra Low-Power IoT Applications.

The aim of this paper is to discuss the usability of vibrations as energy sources, for the implementation of energy self-sufficient wireless sensing platforms within the Industrial Internet of Things (IIoT) framework. In this context, this paper proposes to equip vibrating assets like machinery with piezoelectric sensors, used to set up energy self-sufficient sensing platforms for hard-to-reach positions. Preliminary measurements as well as extended laboratory tests are proposed to understand the behavior of commercial piezoelectric sensors when employed as energy harvesters. First, a general architecture for a vibration-powered LoRaWAN-based sensor node is proposed. Final tests are then performed to identify an ideal trade-off between sensor sampling rates and energy availability. The target is to ensure continuous operation of the device while guaranteeing a charging trend of the storage component connected to the system. In this context, an Ultra-Low-Power Energy-Harvesting Integrated Circuit plays a crucial role by ensuring the correct regulation of the output with very high efficiency.

Flexible and fully integrated 4-bit SAR ADC using metal-oxide TFTs

This paper presents a flexible fully integrated 4-bit successive approximation register (SAR) analog-to-digital converter (ADC), which is fabricated by unipolar n-type indium-zinc-oxide thin-film transistors. A bootstrap switch and a comparator with a pre-amplifier are proposed and employed to realize the SAR ADC. The fabricated SAR ADC achieves a sampling rate of up to 20 kHz and a peak signal-to-noise and distortion ratio of 22.9 dB, corresponding to an effective-number-of-bits of 3.52 bits. The static performance of −0.58/+0.37 LSB DNL and −0.49/+0.39 LSB INL is achieved.

Multi-channel delay sampling to extend imaging depth in high-speed swept-source OCT systems.

We present a multi-channel delay sampling method to extend imaging depth in high-speed swept-source optical coherence tomography (SS-OCT). A balanced detector captures interference signals, converting them into electrical signals, which are then split into N channels, each with fixed time delays determined by the length of electrical cables. Then, they are digitized by an N-channel acquisition card. A calibration procedure is utilized to compensate for non-uniform phase shifts resulting from fixed time delays. The N-channel signals are merged in k-space and resampled to obtain a linearized spectrum, which increases the sampling rate by a factor of N, thereby extending the ranging distance by N times, all without altering k-clock triggering or sacrificing other imaging performance. The signal-to-noise ratio and sensitivity within the original depth range also have been enhanced. This advancement contributes to the improvement of the overall performance of SS-OCT systems.

Anthrapyrazolone functionalized Oligo(ethylene glycol)methyl ether methacrylates based fluorescent polymeric hydrogel for selective detection of Tetracycline drug

Tetracycline (TC) antibiotics are essential for treating bacterial infections; however, overuse and improper disposal place the environment in danger and promote the growth of drug-resistant microorganisms. Monitoring their impact on living organisms is vital. Polymeric-based hydrogels offer promise in antibiotic detection due to their versatility and biocompatibility, despite their uncommon use for this purpose. In this study, we have synthesized a fluorescent polymeric hydrogel, P(OEGMA-co-DIMA) utilizing oligo(ethylene glycol)methyl ether methacrylates (OEGMA) and laboratory-synthesized fluorescent anthrapyrazolone containing monomer, 2-(benzo[1,2,3-cd:4,5,6-c’d]bis(indazole)-2(7H)-yl)ethyl methacrylate (DIMA) for the fluorometric detection of TC in the aqueous medium. The P(OEGMA-co-DIMA) hydrogel exhibited exceptional affinity towards TC while exhibiting high selectivity. The chemosensing behavior of the hydrogel was also compared with the DIMA monomer. Upon adding TC, emission analysis revealed a notable quenching in the emission intensity of the P(OEGMA-co-DIMA) hydrogel as well as the DIMA monomer. The Stern Volmer rate constants (Ksv) of the hydrogel (Ksv = 9.99 x 103 M−1) in water and DIMA (Ksv = 25.63 x 103 M−1) in acetonitrile showed a higher quenching rate constant with a limit of detections (LODs) of 1.3 nM and 0.11 nM. Turn-off fluorescence behavior occurs via a static quenching mechanism through a photoinduced electron transfer process and intermolecular hydrogen bonding. To validate the selectivity, the interference analyses in an aqueous medium with different antibiotics are provided. To rationalize the real-time applicability of the hydrogel, studies were extended to water samples obtained from deionized water, tap water, lake water, and also in milk samples. The recovery rate and LOD for real samples ranged from 83.8% to 111% and 0.83 to 3.6 nM, respectively.

Balancing resistor-based online electrochemical impedance spectroscopy in battery systems: opportunities and limitations

Active dissipative balancing systems are essential in battery systems, particularly for compensating the leakage current differences in battery cells. This study focuses on using balancing resistors to stimulate battery cells for impedance measurement. The value of impedance spectroscopy for in-depth battery cell diagnostics, such as temperature or aging, is currently being demonstrated and recognized by vehicle manufacturers, chip producers, and academia. Our research systematically explores the feasibility of using existing balancing resistors in battery management systems and identifies potential limitations. Here we propose a formula to minimize hardware requirements through signal processing techniques. A quadrupling of the sampling rate, number of averaging values, or the size of the fast Fourier transform is equivalent, concerning the signal-to-noise ratio, to increasing the analog resolution by one bit or reducing the input filter bandwidth by a quarter.

A 12-Bit 1.2-GS/s Current-Steering DAC in 45-NM CMOS Technology

This research paper presents a 12-bit 8–4-segmented current-steering DAC (digital-to-analog converter) that offers notable advantages, particularly in terms of its reduced power consumption compared to other similar designs. The exceptional performance of our DAC can be attributed to two key factors: the use of a thermometer encoder and an efficient digital input bit segmentation. These features contribute to enhanced precision and reduced power requirements, setting our DAC apart from the existing solutions in the field. These enhancements significantly reduce glitches and improve the accuracy of the DAC’s output. DAC dissipates a total power of about 44.95[Formula: see text]mW when running at a sample rate of 1.2[Formula: see text]GHz with the CMOS technology of 45[Formula: see text]nm. While the supply input voltage is held constant at 1.2[Formula: see text]V, the DAC’s digital input is pulsating in nature in which the ON voltage is 1[Formula: see text]V and OFF voltage is 0[Formula: see text]V. Also, the DAC has a spurious-free dynamic range (SFDR) of 41.77[Formula: see text]dB.

High-Efficiency Dynamic Three-Dimensional Topography Measurement Using the Phase Shift Generation Method

In the fringe projection profilometry (FPP), the traditional phase-shifting (TPS) algorithm and the Fourier transform (FT) algorithm are beset with a conundrum where measurement efficiency and conflicts with measurement accuracy, thereby limiting their application in dynamic three-dimensional (3D) measurements. Here, we propose a phase shift generation (PSG) method, which acquires the sinusoidal fringes by sparse sampling and reconstructs the complete phase-shifting sequence by generating the missing fringes with superimposed coupling of adjacent fringes. According to our proposed PSG method in which the sinusoidal fringe sequence size is about half of the TPS method, meaning that the PSG method will be timesaving in the phase-shifting sequence sampling process. Moreover, because of the utilization of multiframe fringes, our PSG method allows for a more accurate measurement than the FT method. Both simulation and experimental results demonstrate that our proposed PSG method can well balance the measurement accuracy and efficiency with a lower sampling rate, bearing a great potential to be applied in both scientific and industrial areas.

Smooth hard shrinkage operator for tensor completion based on self-adaptive transforms

The tensor completion(TC) problem has attracted wide attention due to its significant practical significance and broad application background. Various approaches have been used to solve this problem, including approximating the rank function through its convex envelope, the tensor nuclear norm. However, due to the gap between the rank function and its convex envelope, this approach often fails to achieve satisfactory recovery. Recent developments have highlighted that non-explicit non-convex functions, especially the one induced by the Smooth Hard(SH) shrinkage operator, can approximate the l0 norm more effectively compared to other methods, and this function facilitates the reconstruction of noisy MRI data with minimal sampling rate requirements. In our paper, we implement the SH shrinkage operator within the TC framework, achieving accurate tensor data recovery at lower sampling rates. Moreover, we adopt a method that adaptively generates transformation matrices based on specific features of the data, which, compared to traditional TC models, can better exploit the inherent low-rank properties of tensor data. Additionally, our approach integrates a reweighting strategy to efficiently merge tensors recovered from various dimensions. Finally, a feasible minimization algorithm is proposed, which combines the non-explicit non-convex regularizer and self-adaptive transforms. It is proven that the constructed sequence of the proposed algorithm is a Cauchy sequence and can converge to a stable point. Extensive experimental results demonstrate that our proposed approach can achieve accurate tensor recovery at lower sampling rates compared to other methods.

Enhancing continuous time series modelling with a latent ODE-LSTM approach

Due to their dynamic properties such as irregular sampling rate and high-frequency sampling, Continuous Time Series (CTS) are found in many applications. Since CTS with irregular sampling rate are difficult to model with standard Recurrent Neural Networks (RNNs), RNNs have been generalised to have continuous-time hidden dynamics defined by a Neural Ordinary Differential Equation (Neural ODE), leading to the ODE-RNN model. Another approach that provides a better modelling is that of the Latent ODE model, which constructs a continuous-time model where a latent state is defined at all times. The Latent ODE model uses a standard RNN as the encoder and a Neural ODE as the decoder. However, since the RNN encoder leads to difficulties with missing data and ill-defined latent variables, a Latent ODE-RNN model has recently been proposed that uses a ODE-RNN model as the encoder instead.Both the Latent ODE and Latent ODE-RNN models are difficult to train due to the vanishing and exploding gradients problem. To overcome this problem, the main contribution of this paper is to propose and illustrate a new model based on a new Latent ODE using an ODE-LSTM (Long Short-Term Memory) network as an encoder - the Latent ODE-LSTM model. To limit the growth of the gradients, the Norm Gradient Clipping strategy was embedded on the Latent ODE-LSTM model.The performance evaluation of the new Latent ODE-LSTM (with and without Norm Gradient Clipping) for modelling CTS with regular and irregular sampling rates is then demonstrated. Numerical experiments show that the new Latent ODE-LSTM performs better than Latent ODE-RNNs and can avoid the vanishing and exploding gradients during training.Code implementations developed in this work are available at github.com/CeciliaCoelho/LatentODELSTM.

Measurement of Effective Radium Content and Radon Exhalation Rates in Soil Samples of Adigrat, Tigrai Region, Ethiopia

The predominant source of radiation exposure for the global population is natural radiation, originating from radioactive elements present in the Earth's soil, air, and water. These elements, such as uranium, radium, and radon, have existed since the planet's formation. Prolonged exposure, particularly in specific geographic locations, significantly contributes to radiation risks for populations. Exposure routes include inhaling radon gas, ingesting radioactive substances in food and water, and direct exposure to terrestrial gamma radiation. While natural radiation exposure generally remains within safe limits, certain factors can elevate risks, such as geological formations rich in radioactive minerals or proximity to nuclear facilities. Environmental conditions and geological factors can also influence radiation levels, leading to fluctuations in exposure risks. Monitoring and regulating radiation exposure are crucial to prevent surpassing permissible levels, which can result in health hazards like an increased risk of cancer. Effective management requires ongoing research, stringent regulations, and public awareness efforts to mitigate the risks associated with natural radiation exposure and protect public health. Effective radium content and radon exhalation rates in soil samples collected from Adigrat in Tigrai state of Ethiopia were experimentally measured by ‘‘Sealed Can Technique’’ using LR-115 type II plastic track detectors. The values of effective radium content were found to vary from16.11 Bq/kg to 34.24 Bq/kg with an average value of 25.93 Bq/kg and a standard deviation of 6.10. The mass and surface exhalation rate has been found to vary from 1.61x10<sup>-6</sup> Bq kg<sup>-1</sup> d<sup>-1</sup> to 3.42 x10<sup>-6</sup> Bq kg<sup>-1</sup> d<sup>-1</sup> and 0.81x10<sup>-4</sup> Bq m<sup>-2</sup> d<sup>-1</sup> to 1.71x10<sup>-4</sup>Bq m<sup>-2</sup> d<sup>-1</sup>, respectively. The radium content in soil in the study area is below the permissible value of 370 Bq/kg as recommended by Organization for Economic Cooperation and Development.

Characterisation of chitosan coating incorporated with oleuropein and its effects on the quality of refrigerated chicken breasts

SummaryThe stability, microstructure and antioxidant capacity of the composite coating solution of chitosan (CS) and oleuropein (OLE, 0.08–0.32%, w/v) were estimated by zeta potential, rheological properties, scanning electron microscope and 2,2‐diphenyl‐1‐picrylhydrazyl radical scavenging assays. Effects of the composite coating on refrigerated chicken breasts were evaluated by microbial, chemical and sensory assays. The results revealed that the coating solutions were stable, intact and exhibited good antioxidant activities. When chicken samples were stored at 4 °C for 12 days, the microbial population treated with CS + OLE decreased to 5.43–7.59 log CFU g−1 compared with the control (8.41 log CFU g−1). The increase in pH values, total volatile base nitrogen and thiobarbituric acid reactive substances contents, and drip loss rate of chicken samples had also been deferred. Overall, the results indicated that it was feasible to apply CS with OLE to extend the shelf life of chicken breasts.

Synthesis and Performance Evaluation of Bio-Sourced PO3G Ester Plasticizer in L-Polylactic Acid Thin Films

This study aims to synthesize and evaluate the properties of bio-sourced poly(1,3-propanediol) laurate (PO3G-LA) as a plasticizer in the fabrication of poly(1,3-propanediol) laurate/L-polylactic acid (PO3G-LA/PLLA) thin films. Utilizing an esterification reaction between poly(1,3-propanediol) (PO3G) and lauric acid (LA), PO3G-LA is synthesized and incorporated into PLLA films via solution casting. Results demonstrate that PO3G-LA exhibits excellent compatibility with PLLA, markedly enhancing the toughness and slightly improving the thermal stability of the resulting films. Specifically, the addition of 20% PO3G-LA increases the elongation at the break of the films to 190%, indicating enhanced flexibility. Accelerated degradation experiments conducted at 60 °C revealed that the presence of PO3G-LA under neutral conditions had minimal impact on the degradation rate of the film samples. Conversely, variations in the PO3G-LA content of the films when exposed to acidic and alkaline conditions were found to influence their degradation rates.

PSCAT: a lightweight transformer for simultaneous denoising and super-resolution of OCT images

Optical coherence tomography (OCT), owing to its non-invasive nature, has demonstrated tremendous potential in clinical practice and has become a prevalent diagnostic method. Nevertheless, the inherent speckle noise and low sampling rate in OCT imaging often limit the quality of OCT images. In this paper, we propose a lightweight Transformer to efficiently reconstruct high-quality images from noisy and low-resolution OCT images acquired by short scans. Our method, PSCAT, parallelly employs spatial window self-attention and channel attention in the Transformer block to aggregate features from both spatial and channel dimensions. It explores the potential of the Transformer in denoising and super-resolution for OCT, reducing computational costs and enhancing the speed of image processing. To effectively assist in restoring high-frequency details, we introduce a hybrid loss function in both spatial and frequency domains. Extensive experiments demonstrate that our PSCAT has fewer network parameters and lower computational costs compared to state-of-the-art methods while delivering a competitive performance both qualitatively and quantitatively.

Comparison of Candida colonization in intensive care unit patients with and without COVID-19: First prospective cohort study from Turkey.

Candida species are the primary cause of fungal infections in intensive care units (ICUs). Despite the increasing prevalence of Candida-related infections, monitoring the progression of these infections from colonization in COVID-19 ICU patients lacks sufficient information. This study aims prospectively to compare 62 COVID-19 and 60 non-COVID-19 ICU patients from admission to discharge in terms of colonization development, rates, isolated Candida species, risk factors, and Candida infections during hospitalization. A total of 1464 samples were collected at specific time intervals from various body sites [mouth, skin (axilla), rectal, and urine]. All samples were inoculated onto CHROMagar Candida and CHROMagar Candida Plus media, and isolates identified using MALDI-TOF MS. COVID-19 patients exhibited significantly higher colonization rates in oral, rectal, and urine samples compared to non-COVID-19 patients, (p<0.05). Among the Candida species, non-albicans Candida was more frequently detected in COVID-19 patients, particularly in oral (75.8%-25%; p<0.001) and rectal regions (74.19% - 46.66%; p<0.05). Colonization with mixed Candida species was also more prevalent in the oropharyngeal region (p<0.05). Mechanical ventilation and corticosteroid use emerged as elevated risk factors among COVID-19 patients (p<0.05). Despite the colonization prevalence, both COVID-19-positive and negative patients exhibited low incidences of Candida infections, with rates of 9.67% (n=6/62) and 6.67% (n=3/60), respectively. Consequently, although Candida colonization rates were higher in COVID-19 ICU patients, there was no significant difference in Candida infection development compared to the non-COVID-19 group. However, the elevated rate of non-albicans Candida isolates highlights potential future infections, particularly given their intrinsic resistance in prophylactic or empirical treatments if needed. Additionally, the high rate of mixed colonization emphasizes the importance of using chromogenic media for routine evaluation.

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Wind energy is a renewable resource with abundant reserves, and its sustainable development and utilization are crucial. The components of wind turbines, particularly the blades and various surfaces, require meticulous defect detection and maintenance due to their significance. The operational status of wind turbine generators directly impacts the efficiency and safe operation of wind farms. Traditional surface defect detection methods for wind turbines often involve manual operations, which suffer from issues such as high subjectivity, elevated risks, low accuracy, and inefficiency. The emergence of computer vision technologies based on deep learning has provided a novel approach to surface defect detection in wind turbines. However, existing datasets designed for wind turbine surface defects exhibit overall category scarcity and an imbalance in samples between categories. The algorithms designed face challenges, with low detection rates for small samples. Hence, this study first constructs a benchmark dataset for wind turbine surface defects comprising seven categories that encompass all common surface defects. Simultaneously, a wind turbine surface defect detection algorithm based on improved YOLOv5 is designed. Initially, a multi-scale copy-paste data augmentation method is proposed, introducing scale factors to randomly resize the bounding boxes before copy-pasting. This alleviates sample imbalances and significantly enhances the algorithm’s detection capabilities for targets of different sizes. Subsequently, a dynamic label assignment strategy based on the Hungarian algorithm is introduced that calculates the matching costs by weighing different losses, enhancing the network’s ability to learn positive and negative samples. To address overfitting and misrecognition resulting from strong data augmentation, a two-stage progressive training method is proposed, aiding the model’s natural convergence and improving generalization performance. Furthermore, a multi-scenario negative-sample-guided learning method is introduced that involves incorporating unlabeled background images from various scenarios into training, guiding the model to learn negative samples and reducing misrecognition. Finally, slicing-aided hyper inference is introduced, facilitating large-scale inference for wind turbine surface defects in actual industrial scenarios. The improved algorithm demonstrates a 3.1% increase in the mean average precision (mAP) on the custom dataset, achieving 95.7% accuracy in mAP_50 (the IoU threshold is half of the mAP). Notably, the mAPs for small, medium, and large targets increase by 18.6%, 16.4%, and 6.8%, respectively. The experimental results indicate that the enhanced algorithm exhibits high detection accuracy, providing a new and more efficient solution for the field of wind turbine surface defect detection.

Effect of Mg incorporation on the properties of PCL/Mg composites for potential tissue engineering applications

Polycaprolactone (PCL) is a biocompatible polymer readily moldable into various shapes and designs. However, its low mechanical strength and slow biodegradation restrict its use in tissue engineering. Magnesium (Mg), a biocompatible metal with excellent osteoconductivity and biodegradability, is a promising choice for tissue engineering applications. This study investigates the influence of Mg incorporation on the properties of PCL/Mg composites, aiming to evaluate their suitability for 3D-printable (3DP) tissue engineering applications. We synthesized a series of PCL/Mg composites with varying Mg concentrations and characterized their mechanical, thermal, and degradation properties. According to microscopic analysis of the composite films, the Mg particles are dispersed consistently throughout all the compositions. The findings demonstrated that adding Mg influenced PCL’s mechanical and thermal properties. The mechanical test results showed that the tensile strength of 15% Mg composite filaments improved by around 10% compared to the neat PCL filaments. However, the elastic modulus decreased by around 50% for the same composition. The thermal study revealed a significant reduction in the degradation temperature from above 400°C for pure PCL to around 300°C for PCL/Mg composite having 15% Mg. Additionally, the weight loss during in vitro degradation showed that the presence of Mg had significantly increased the degradation rate of composite samples. Also, Mg incorporation influences cell adhesion, with better attachment observed for 10% Mg 3DP samples. Overall, PCL/Mg composites offer a solution to overcome the limitation of low thermo-mechanical properties typically associated with the PCL.

Integration of the multivariate statistical control chart and machine learning to identify faults in the quality characteristics for polylactic acid with glass fiber composites in injection molding

Complex processing parameters need to be adjusted for expected qualities in injection molding processing. Once the process is abnormal, it is essential to spend time and human work on fault diagnosis. In this study, we focus on fault diagnosis of injection molding processing parameters for polylactic acid/glass fiber composites. The injection molding processing parameters include the melt temperature, injection speed, packing pressure, packing time, and cooling time. The qualities include tensile strength, hardness, impact strength, and flexure strength. When processing parameters deviate from the optimal process condition, the multivariate statistical control chart monitors downgraded qualities. The machine is operated at the optimal process conditions to generate normal samples and the corresponding four qualities of data are chosen as the historical data. Hotelling’s T2 is used to calculate the upper control limit (UCL) from the historical data to detect abnormal samples. If the T2 value exceeds the UCL, the corresponding sample is considered abnormal. Then, the residuals of qualities for abnormal samples are obtained by a residual control chart. They are chosen as the feature values for the backpropagation neural network (BPNN) to identify the abnormal processing parameters. The experimental results proved that the BPNN can achieve a 100% recognition rate for single-factor abnormal samples. For the single-/double-factor mixture, the accuracy rate of double-factor classification can reach 97.44%. This proposed study has the advantage of high stability, being non-destructive, high precision, and low cost, and can be widely promoted in injection molding industries.

Intelligent condition monitoring with CNN and signal enhancement for undersampled signals

High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.

Serosurvey of Coxiella burnetii in Police Officers and Working Dogs in Brazil: Case Report and One Health Implications.

Although the Coxiella burnetii infection has been investigated in dogs, its role in human transmission remains to be fully established, particularly in close and daily human-dog contact settings, such as in Police K-9 Units. Accordingly, this study aimed to assess anti-C. burnetii antibodies in clinically healthy police officers by an in-house indirect immunofluorescence assay (IFA), and working dogs by a commercial IFA Kit, from the State Special Operations Battalion, Paraná, Southern Brazil. Overall, 1/18 (5.5%) police officers and 9/30 (30.0%; CI 95% 16.66-47.88) dogs tested seropositive to anti-C. burnetii IgG antibodies. To date, this is the highest prevalence of Q fever seropositivity among military dogs worldwide. Despite the low sampling rate, a statistically significant association was found between seropositivity and female dogs (p = 0.0492). Further studies with larger sample sizes should be conducted to establish the prevalence of Q Fever in other Brazilian K-9 Units. In summary, this study is the first to conduct a concomitant serosurvey of police officers and working dogs, and its findings should be considered a warning for cross-exposure and transmission of Coxiella burnetii among Police K-9 Units in Brazil and worldwide.