High Sampling Frequency Research Articles

Data analysis procedures to improve energy resolution in silicon detectors using GET electronics

This study primarily focuses on improving the energy resolution of Double-Sided Silicon Strip Detectors (DSSSD) through optimized data analysis procedures using General Electronics for Time Projection Chambers (GET). We introduce two edge identification algorithms that demonstrate comparable efficacy. These methods are subsequently integrated with various techniques for measuring pulse amplitude, including sample smoothing and pole-zero cancellation. Notably, sample smoothing significantly enhances performances, achieving a resolution of 0.33% with DSSSD α calibration data. Additionally, we describe and apply the trapezoidal filter, examining its impact on resolution improvement and obtaining comparable results. The study also evaluates how internal GET parameters, specifically the sampling frequency and the shaping time of the Sallen-Key (SK) low-pass filter, affect signal resolution. Higher sampling frequencies and lower values of the SK filter were found to increase performance.

Measuring daily tourism mobility spillover at the intra-metropolis level with mobile positioning data

ABSTRACT Although tourism mobility spillover continues to be a key indicator for tourism management, more innovative research must be conducted at the micro level and high sampling frequency. Against the backdrop of an increasing number of global cities, in this paper, we evaluate the daily tourism mobility spillover inside a worldwide city of China: Shanghai. Based on the Granger causal network model and an original mobile positioning dataset, we analyse the causal relationship between local tourism flows and the spillover effects of tourism mobility within Shanghai. By categorising tourists into ‘local tourists from Shanghai’ and ‘tourists from out of Shanghai’, we reveal a significant causal relationship between Shanghai districts and flows generated by ‘tourists from out of Shanghai’. The analysis of the causal network structure also reveals key districts and points of interest that significantly contribute to congestion in tourism mobility and Shanghai's dynamics. This econometric approach offers policymakers a valuable tool to monitor mobility drivers and optimise flows within the city.

Near-surface particulate backscattering observations with bio-optical Lagrangian drifters

Abstract Particulate matter concentration is an essential ocean variable (EOV) useful for understanding biogeochemical processes, improving ocean productivity estimates, and constraining coupled physical and biogeochemical numerical models. While direct observations of this parameter are difficult to obtain, the optical backscattering coefficient of marine particles (bbp) can be used as a reliable proxy. However, most in-situ multi-spectral bbp measurements are ship-borne or obtained from moored systems, thus with a limited spatial and temporal coverage. Utilizing Surface Velocity Programme (SVP) drifting buoys with self-contained backscatter sensors designed for extended deployment periods offers a promising solution for collecting data in challenging marine environments. In this study, we presented for the first time the integration of a commercially available, high-frequency and multispectral optical backscatter sensor into an SVP drifter, the so-called BO-SVP drifter designed to collect bbp measurements near the ocean surface. bbp measurements obtained by the BO-SVP drifter were reliable over a wide range of environmental conditions, showing a good agreement with independent datasets (relative bias < 10%; relative standard deviation < 36%). The BO-SVP drifter captures the satellite sub- and pixel scale variability by combining the Lagrangian approach and a high frequency sampling. This configuration enables the acquisition of measurements across numerous pixels in a single day, enhancing validation activities for ongoing and future satellite products and the quantification of associated uncertainties. The measurements acquired by these platforms can offer valuable insights into particle distribution at fine spatial scales, daily variability, and its relationship with water masses type and ocean dynamics.

Autoantibodies towards HFE and SYT5 in anti-neutrophil cytoplasm antibody-associated vasculitis relapse.

Identification of those at high and low risk of disease relapse is a major unmet need in the management of patients with ANCA-associated vasculitis (AAV). Precise stratification would allow tailoring of immunosuppressive medication. We profiled the autoantibody repertoire of AAV patients in remission to identify novel autoantibodies associated with relapse risk. Plasma samples collected from 246 AAV patients in remission were screened for novel autoantibodies using in-house generated protein arrays including 42 000 protein fragments representing 18 000 unique human proteins. Patients were categorized based on the occurrence and frequency of relapses. We modelled the association between these antibodies and relapse occurrence using descriptive and high dimensional regression approaches. We observed nine autoantibodies at higher frequency in samples from AAV patients experiencing multiple relapses compared with patients in long-term remission off therapy (LTROT). LASSO analysis identified six autoantibodies that exhibited an association with relapse occurrence after sample collection. Antibodies targeting HFE and SYT5 were identified as associated with relapse in both analyses. Through a broad protein array-based autoantibody screening, we identified two novel autoantibodies directed against HFE and SYT5 as candidate biomarkers of relapse in AAV.

Comparing Kurtosis-adjusted weighted levels with other metrics to assess the risk of hearing loss from non-Gaussian noise exposures

Several studies have shown that impulsive noise can cause more damage to hearing than steady-state noise of equal energy. As a result, a large body of research has been devoted to evaluating the hazards of impulsive noise. However, work environments often have varying noise patterns, including Gaussian background noise combined with high-level transient noises contributing to the worker's daily dose. Thus, an energy-based noise metric underestimates the risk of hearing loss unless incorporating a temporal structure correction term. Kurtosis has been reported as an effective adjunct to energy for predicting hearing hazards from non-Gaussian noise exposures. In this work, recordings of real impulsive noise were conducted using a portable measuring system at a very high sampling frequency and using high-pressure microphones. Controlled synthetic non-Gaussian noise was generated using these impulsive signals and steady-state background noise. After exploring the detailed temporal structure of the noise, the daily exposure time has been determined using different metrics, including kurtosis-adjusted weighted levels, weighted peak levels, and auditory risk units. The examples presented here aim to demonstrate how kurtosis can help develop more accurate methods to prevent noise-induced hearing loss.

Presença de salmonella em cloaca de Jabuti-piranga (Chelonoidis carbonaria)

The objective of this study was to evaluate the presence of Salmonella in tortoises (Chelonoidis carbonaria) in relation to their breeding environment, since there is human-animal proximity, which can lead to an amphixenosis. The interaction between humans and animals is a determining factor in the increased susceptibility to transmission of certain zoonoses, since some of these animals, often unconventional pets, may be of illegal origin. Animal trafficking becomes evident as it is a major contributor to the spread of pathogens, due to several factors. Our research extended from November 2023 to March 2024, seeking to demonstrate the presence of Salmonella spp in tortoises. The present study was carried out through swabs that were gently rubbed onto the cloaca of the animals. Soon after, the bacterial culture process was started in a petri dish with the following culture media: SS Agar and EMB Agar. The research showed a high frequency of Salmonella and, in disagreement with the other research, a small frequency of Escherichia coli was found. It was possible to conclude that the evaluated samples had a high frequency of positive samples for salmonella.

Validation of the RR Lyrae period determination in the Pan-STARRS PS1 3π survey with K2

Context. The Pan-STARRS 3π survey has detected hundreds of thousands of variable stars thanks to its coverage and 4-year time span, even though the sampling of the light curves is relatively sparse. These light curves contain only 10–15 detections in each of the five filters (g, r, i, z, y). During the K2 mission, the Kepler space telescope observed along the ecliptic plane with a high sampling frequency, although only for about 80 days in each of its campaigns. Aims. Crossmatching and investigating the RR Lyrae stars observed by both K2 and Pan-STARRS can serve as a valuable tool to validate the classification and period determination of the ground-based survey. Methods. We used the Sesar catalogue of RR Lyrae stars detected by Pan-STARRS. After determining the overlap between the stars observed by both Pan-STARRS and K2, we also considered the Gaia DR3 SOS RR Lyrae catalogue data for the list of these stars wherever it was available. The frequencies of the light variations were calculated by applying the Lomb-Scargle periodogram method on the K2 light curves that were prepared with autoEAP photometry. The calculated frequencies of the stars then were compared with those given in the Sesar catalogue and the Gaia DR3 RR Lyrae catalogue. Results. We found that for the majority of the stars, the classification (95.6%) and the frequency determination (90.1%) of the Pan-STARRS RR Lyrae stars were consistent within 0.03 d−1 with those that we derived from the K2 autoEAP light curves. For a significant subset of the sample, 7.4%, however, an offset of 1 or 2 d−1 was found in the frequencies. These are the result of the sampling of the detections, because Pan-STARRS observations are affected by diurnal cycles, whereas Kepler carried out measurements quasi-continuously. We found that RRc subtypes are significantly more affected (25.3%) than RRab subtypes (3.7%), which is most likely caused by RRc stars having less sharp light curve features. Validation via space-based data will be important for future ground-based surveys, as well.

Pilot protection based on the similarity of the traveling waves between the quadratic fitting curve and the calculated curve for the All-DC wind power transmission system

The All-DC wind power generation system can effectively solve the harmonic resonance problem caused by large-scale wind power AC collection and transmission, gradually becoming a hot topic in future research. However, there are few studies on the relay protection of the All-DC wind power transmission systems. In the All-DC wind power systems, there are differences in topology, control strategies, fault characteristics, boundary characteristics, etc. At the same time, existing pilot protection has problems such as strict information synchronization requirements and selection of setting values through simulation. Therefore, the proposed pilot protection based on the similarity of the traveling wave between the quadratic fitting curve and the calculated curve for the All-DC wind power system transmission lines. Firstly, the difference of the 1-mode initial current waveform characteristics is analyzed in the time domain at both ends. Then, the least squares method is introduced to fit the differential curves of the current forward and backward traveling waves at two terminals. The differential characteristics of the differences between the fitted curve, the calculated curve, and the measured curve under the influence of internal and external errors are analyzed. Finally, the calculated value of traveling wave current during external metallicity fault is used as the standard. By comparing the fitting curve of current traveling waves and the difference in the calculation curve to distinguish internal faults from external faults. The simulation results show that the proposed protection principle can fault resistance of 2200 Ω and noise interference of 20 dB. In addition, the proposed method does not require strict data synchronization, higher sampling frequency, and setting value through simulation.

Probabilistic hydrogen-assisted fatigue crack growth under random pressure fluctuations in pipeline steels

The increasing demand for energy and the global threat of climate change have driven the search for alternative energy sources, with hydrogen emerging as a prominent substitute for fossil fuels. The fatigue behavior of pipeline steels under gaseous hydrogen is a critical problem that is impeding the industry's adoption of hydrogen into the current natural gas infrastructure. A brief review of existing hydrogen-assisted fatigue crack growth (HA-FCG) studies, which reveal several key gaps, is given first. Existing HA-FCG models predominantly address constant amplitude loading, while the realistic driving force is random loading in gas pipelines. Also, the current uncertainty quantification studies for HA-FCG focus on material randomness and overlook the large uncertainties associated with random pressure fluctuations. To address these issues, this study proposes a HA-FCG model that utilizes a time-based subcycle approach, allowing for direct application to random spectrum loads without the need for cycle counting. A model parameter as a function of hydrogen operating conditions is introduced to capture the different regimes in HA-FCG, and the model predictions are compared with ASME B31.12 code. Following this, statistical analysis of random pressure fluctuation data collected from natural gas pipelines at multiple locations is performed. The realistic industry pressure data shows distinct statistical features, and it is observed that the high-fidelity data (high sampling frequency) is beneficial for accurate fatigue life predictions. Uncertainty quantification and load reconstruction are performed by the Karhunen–Loève expansion with a post-clipping procedure, leading to a probabilistic HA-FCG analysis. The paper concludes with key findings and suggests directions for future research.

A new method for tracking the preparatory activation of target templates for visual search with high temporal precision.

Efficiently selecting task-relevant objects during visual search depends on foreknowledge of their defining characteristics, which are represented within attentional templates. These templates bias attentional processing toward template-matching sensory signals and are assumed to become anticipatorily activated prior to search display onset. However, a direct neural signal for such preparatory template activation processes has so far remained elusive. Here, we introduce a new high-definition rapid serial probe presentation paradigm (RSPP-HD), which facilitates high temporal resolution tracking of target template activation processes in real time via monitoring of the N2pc component. In the RSPP-HD procedure, task-irrelevant probe displays are presented in rapid succession throughout the period between task-relevant search displays. The probe and search displays are homologously formed by lateralized "clouds" of colored dots, yielding probes that occur at task-relevant locations without confounding template-guided and salience-driven attentional shifts. Target color probes appearing at times when a corresponding target template is active should attract attention, thereby eliciting an N2pc. In a condition where new probe displays appeared every 50 ms, probe N2pcs were reliably elicited during the final 800 ms prior to search display onset, increasing in amplitude toward the end of this preparation period. Analogous temporal profiles were also observed with longer intervals between probes. These findings show that search template activation processes are transient and that their temporal profile can be reliably monitored at high-sampling frequencies with the RSPP-HD paradigm. This procedure offers a new route to approach various questions regarding the content and temporal dynamics of attentional control processes.

Interannual variability and seasonality of litterfall in three temperate and boreal forest ecosystems of eastern Canada: A synthesis of long-term monitoring

Litterfall is a major pathway for transferring aboveground biomass to the forest floor and thus plays an important role in building forest soil carbon stocks. However, inter- and intra-annual variability of litterfall remains poorly documented, especially in North American temperate and boreal forests, due to the lack of recent long-term studies at high sampling frequencies. This potentially creates uncertainties in estimates of forest carbon budget models. The objectives of the present study were to 1) quantify the mean annual flux, interannual variability, and seasonality of litterfall in three sites (dominated respectively by sugar maple (Acer saccharum Marsh.), balsam fir (Abies balsamea (L.) Mill. 1768), and black spruce (Picea mariana (Mill.) B.S.P.)) in eastern Canada over a period of 22–32 years, 2) relate the litterfall amounts and temporal variations to the changes in the size of major organic matter pools in these ecosystems, and 3) compare our litterfall estimates with reference values used in national greenhouse gas inventories. Litterfall production decreased from the sugar maple to the balsam fir and black spruce sites, preponderantly due to species composition. Litterfall evolution was related to the aboveground biomass of live trees in both conifer sites; in contrast, in the broadleaf site, changes in forest composition and structure were apparently the main drivers. The litterfall seasonality varied between broadleaf and conifer sites and could be explained by a sigmoidal model. Substantial departures from the seasonality for some given years were likely due to important climatic anomalies. Forest floor biomass remained stable over time at all three sites despite the increase in litterfall at the balsam fir and sugar maple sites and rapid forest floor turnover at the latter site. Our analyses of litterfall suggest that reference values from the literature used for national greenhouse gas inventories underestimate annual litterfall and forest floor carbon stocks for temperate and boreal forests.

Advancing hospital infection surveillance: Automated detection of hospital-onset bacteremia in a large university hospital

Background: Traditional manual surveillance of healthcare-associated infections often neglects certain hospital areas. This study investigated the potential of automated hospital-onset bacteremia (HOB) detection to enhance surveillance comprehensiveness. Our focus was on assessing baseline HOB levels across a large university hospital and demonstrating the algorithm’s ability to discern infection patterns in different hospital areas. Method: Conducted at one of the three sites of Charité university hospital in Berlin, Germany, our study employed an automated HOB detection algorithm, collaboratively developed with the PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network. HOB was defined as a positive blood culture (BC) with a recognized pathogen or two positive BC with common commensals, occurring two days post-admission or later. A retrospective data analysis spanned 2018-2022, with data extraction in January 2024. HOB rates were calculated per 1,000 patient-days and correlated with BC sampling frequency. Trends over time were examined. A comparison was made between intensive care units (ICU) and non-ICU. Result: Data from 58 wards (10 ICU, 48 non-ICU) with 262,058 in-patient admissions and 1,634,793 patient-days were included. Baseline characteristics of the hospital site are summarized in Table 1. Aggregated HOB rates over a 5-year period (Table 2) were substantially higher in ICU compared to non-ICU, applicable to all HOB, HOB with common commensals, and HOB with pathogens. A scatterplot (Figure 1) illustrates a strong correlation between BC sampling frequency and HOB rates in ICU, while Figure 2 displays HOB rates and BC sampling frequency in ICU over time. Notably, HOB rates were higher in the years 2020 and 2021, followed by a decrease in 2022, despite a continually high BC sampling rate. Discussion: The algorithm consistently detected HOB, providing insights into distinctions between ICU and non-ICU. While fewer HOB occurred in non-ICU, continuous monitoring remains crucial, given that the total number of HOB in non-ICU was higher than in ICU, and HOB in non-ICU might currently be overlooked by established surveillance activities. The automated system also demonstrated its effectiveness in identifying trends over time. Although speculative, it appears likely that the higher HOB rates in 2020 and 2021 were at least partially attributable to the COVID-19 pandemic and accompanying factors. There was a notable correlation between HOB and BC sampling frequency. However, the fact that HOB incidence in ICU decreased in 2022 despite persistently high BC sampling frequency rates, indicates the influence of other factors on HOB rates.

A subspace parameter identification method for nonlinear structures under oversampling conditions

In engineering practice, sampling frequencies much higher than the Nyquist frequency are often used to obtain accurate measurements of the dynamic characteristics of nonlinear systems. However, it may lead to oversampling when using the nonlinear subspace identification method (NSIM). This study proposes a subspace parameter identification method to address the identification of nonlinear structures under oversampling conditions. The proposed method first employs the prediction error method (PEM) to estimate the linear part of the state space model. In noisy environments, the PEM can achieve more accurate results than the NSIM by minimizing an error criterion function. The initial values fed into the PEM are obtained by the stochastic subspace identification method. The nonlinear part of the state space model is estimated by solving a linear regression equation. The proposed method effectively reduces the impact of high sampling frequencies on identification accuracy by combining data with different sampling frequencies to construct the linear regression equation. Among them, the low sampling frequency data is obtained by downsampling the high sampling frequency measurement data. The proposed method effectively reduces the effect of high sampling frequencies and environmental noise on identification accuracy by accurately estimating both the linear and nonlinear parts of the state space model. The identification accuracy of the proposed method is numerically verified by a chain-type structure with five degrees of freedom and a cantilever beam structure. Additionally, experimental verification is carried out on a two degrees of freedom floor structure with local nonlinearities. The results show that the proposed method achieves high identification accuracy in oversampling conditions and noisy environments.

Seven Things to Know About Exercise Classification With Inertial Sensing Wearables.

Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical-therapy monitoring and provide insight on how data collection choices may impact classifier performance. We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units (IMUs) on the chest, pelvis, wrists, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. Exercise groups (n = 10) could be classified with 96% accuracy using a set of 10 IMUs and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

Advanced testbed to assess disturbances in electrical grids with DERs using relays/meters with varying sampling frequencies

Distributed energy resources (DERs) are vital to achieve clean energy goals, but in some cases, their rapid deployment has led to negative effects on their reliability because of possible voltages and/or current disturbances that need to be analyzed in detail. This study created an advanced testbed with a real-time simulator and synchronized time and trigger event systems was created to compare event disturbances from intelligent electronic devices (IEDs) with different sampling frequencies and locations in an electrical grid containing DERs. This testbed is capable of capturing the recorded events of multiple IEDs and the real-time simulator to assess the effect of the disturbances and IEDs’ behaviors, including phase voltages, phase current waveforms, and total harmonic distortion (THD) analysis. In this study, a three-line-to-ground electrical fault was simulated, the disturbance event was detected, and recorded events (from 4 to 133 samples per cycle) were collected from meters and relays located at different sites. A harmonic peak algorithm, which was based on measuring the phase voltage and phase current THD peaks at the first one-cycle of the disturbance events, was created to assess and detect the fault states and locations. Several important findings resulted from performing this study, such as selecting IEDs with high sampling frequencies, installing all IEDs with similar sampling frequencies, assessing the software limitations for computing THD, studying the effect of the IEDs’ locations (downstream or upstream) with respect to the electrical fault site and the distance from the disturbance event point, evaluating the percentage errors of the phase voltage THD peaks at the first one-cycle of disturbance events, and installing synchronized time source and trigger event signal systems for recording the IEDs’ events.

Technical note: Challenges in detecting free tropospheric ozone trends in a sparsely sampled environment

Abstract. High-quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity of maintaining high sampling frequency, in addition to continuity, is often under-appreciated. A common assumption is that, so long as long-term records (e.g., a span of a few decades) are available, (1) the estimated trends are accurate and precise, and (2) the impact of small-scale variability (e.g., weather) can be eliminated. In this study, we show that the undercoverage bias (e.g., a type of sampling error resulting from statistical inference based on sparse or insufficient samples, such as once-per-week sampling frequency) can persistently reduce the trend accuracy of free tropospheric ozone, even if multi-decadal time series are considered. We use over 40 years of nighttime ozone observations measured at Mauna Loa, Hawaii (representative of the lower free troposphere), to make this demonstration and quantify the bias in monthly means and trends under different sampling strategies. We also show that short-term meteorological variability remains a cause of an inflated long-term trend uncertainty. To improve the trend precision and accuracy due to sampling bias, two remedies are proposed: (1) a data variability attribution of colocated meteorological influence can efficiently reduce estimation uncertainty and moderately reduce the impact of sparse sampling, and (2) an adaptive sampling strategy based on anomaly detection enables us to greatly reduce the sampling bias and produce more accurate trends using fewer samples compared to an intense regular sampling strategy.

#2324 Do urinary tract infections trigger chronic inflammation in the kidney allograft? Interleukin 17 levels in the urine of kidney recipients

Abstract Background and Aims Urinary tract infections (UTI) represent the most common complication following kidney transplantation. Their role in triggering immune phenomena in kidney allograft remains unclear. Interleukin 17 (IL17) represents the significant branch in antimicrobial immunity. At the same time IL17 has been found associated with autoimmune inflammatory disorders. The triggering role of infections in autoimmunity as well as the similarities between autoimmunity and post-transplant immunity have been postulated. The aim of the study was to analyze the levels of urine IL 17A levels in relation to prior UTI history and the timeframe after the last UTI episode. Method The group of 91 kidney recipients who received kidney transplantation between 2016 and 2019 was analyzed. The set of 233 urine samples (109 from females and 124 from males) obtained within first 24 months post-transplant was investigated, among these 144 samples from the patients with no previous UTI history and 89 samples from the patients with previous UTI history (1-22 months after the last episode, average 6.7 ± SD 5.7). Furthermore, 18 samples obtained at the time of an UTI episode and 29 samples from healthy controls were included. Urine IL 17A levels were measured with Human IL 17 ELISA Kit (Sigma Aldrich) and urine IL/creatinine concentrations ratio was calculated for each value. Szapiro-Wilk test was used to assess data distribution, Kruskal-Wallis test was used to assess the differences between independent groups and two proportions Z test was used to compare the groups compositions. Results Interleukin 17A appeared detectable in 105 out of the 233 investigated samples (45%), with significantly higher frequency in samples from the recipients with previous UTI history (p = 0.02). The difference was not significant in the group who underwent recurrent UTI episodes vs. these after a single UTI episode. Among the samples in which IL 17A was detected, the period between the last UTI episode and sample collection was divided into three timeframes: 1-3 months, 4-12 months and 12-24 months. Urine IL17A levels appeared significantly higher independently in each timeframe post-UTI when compared to the samples from the recipients without UTI history and controls (p = 0.0001). Fig. 1 It was apparent within the whole group but also in males and females separately. Further, urine IL17A levels were significantly higher in males 1-3 months after the last UTI episode than in females in the analogous period (p = 0.001). Conclusion Interleukin 17A was more frequently detected in urine samples from kidney recipients with previous UTI history. UTI history impacted IL17A urine levels in the analyzed kidney recipients. Within the first 24 months post-transplant, these were higher in the recipients with UTI history prior to the sample collection and it was independent on the timeframe between the last UTI episode and the sample collection. The factors other than UTI history which influence urine IL17A detectability are not fully recognized and remain under investigation.

Blood laboratory analyses preceding in-hospital cardiac arrest: A matched case-control study.

Whether blood laboratory analyses differ in patients who later suffer in-hospital cardiac arrest (IHCA) compared to other hospitalised patients remains unknown. The aim of this study was to describe pre-arrest sampling frequencies, results, and trends in blood laboratory analyses in patients with IHCA compared to controls. This study was a matched case-control study using national registries in Denmark. Cases were defined as patients with IHCA from 2017 to 2021. Controls were defined as hospitalised patients and were matched on age, sex,and date and length of admission. Data on a total of 51 different blood laboratory analyses were obtained. The laboratory analyses of primary interest were lactate, sodium, potassium, and haemoglobin. The index time for cases was defined as the time of cardiac arrest, and a corresponding index time was defined for controls based on the time to cardiac arrest for their corresponding case. Blood sampling frequencies were reported for blood laboratory analyses obtained either within the last 24 hbefore the index time or between the time of hospital admission and the index time. Blood sampling results were reported for blood laboratory analyses obtained within the last 24 h before the index time. A total of 9268 cases and 92,395 controls were included in this study. Cases underwent more frequent sampling of all blood laboratory analyses compared to controls. This higher sampling frequency was more pronounced for lactate compared to sodium, potassium, or haemoglobin. The last measured lactate was higher in cases (median [IQR]: 2.3 [1.3, 4.9]) compared to controls (median [IQR]: 1.3 [0.9, 2.0]). Differences in sodium, potassium, and haemoglobin were negligible. The proportion of abnormally elevated levels of lactate and potassium increased as time to cardiac arrest decreased; no such effect was seen in controls. No temporal trend was evident for sodium or haemoglobin. Patients with IHCA undergo more frequent blood sampling prior to IHCA and have higher levels of lactate compared to matched controls.

Effects of the freeze–thaw process on sources and pathways of subsurface flow in an alpine hillslope on the northeastern Qinghai-Tibet Plateau

The impact of the freeze–thaw process on the active layer is reflected in the changed subsurface flow (SSF) process in cold alpine regions. Identifying sources and pathways of SSF in the freeze–thaw process is critical but difficult, and the related dominant factors and mechanisms are still unknown. In this paper, the effective identification and analysis of SSF are promoted based on field sampling data from the thawing (June) to freezing (September) period of 2022 in the Qinghai Lake basin on the northeastern Qinghai-Tibetan Plateau. By the proposed method with a high sampling frequency and refined sampling spatial scale, the sources and pathways of SSF are clearly identified. The results are as follows: (1) The soil temperature is considered the most fundamental factor affecting the SSF pathways, it influences water infiltration to the deep layer and the effect is extended to the saprolite and weathered bedrock layers. (2) Thawing promotes water to infiltrating into deep layer. 30 cm soil water contributes the most to SSF (2 %–86 %) in the thawing period, while the contribution difference of the water from the 30 cm, 60 cm, and 90 cm layers is small (ranging from 32 %–33 %, 24 %–26 %, and 32 %–35 %, respectively) in the thawed period. (3) Meanwhile, the soil water from different slope positions contribute differently to SSF, and the SSF from deep soil layer is transit in prolong paths and depths. It is caused by the out-of-sync water transit process in the hillslope. With continuing climate warming, we propose that the differences in the water sources of SSF across soil layers may decrease, while the differences in the transit processes of SSF across soil layers may increase.

Using a frequency vibration approach, examine the impact of screw rotor clearance on the screw housing in an 11kW compressor

A screw compressor is one of the industrial machines needed for productivity and use in today's production. Industrial machines generally use pneumatic systems with the help of compressed air supplied by a compressor machine. The compressor working system must receive very special attention. Maintenance and prevention of damage to air screw compressor machines is very necessary, especially maintenance and prevention of damage to rotary screw air compressors. Problems that often occur with the shaft will result in reduced air supply flow or unstable air pressure resulting in disruption of the production line. The aim of this research was to discuss the vibration values produced against the rotor clearance from the ideal output on male and female screw compressor rotors using the frequency analysis method, taking the vibration values at the screw compressor housing point using an Accelerometer sensor and a Fast Fourier Transform (FFT) tool. The analyzer rotates at 1500 rpm in the X, Y and Z axes at each predetermined point. Research shows that the clearance is set at 0.03, 0.05 and 0.08. In this research, the high frequency sample values of 1x 25 Hz, 2x 50 Hz, and 3x 75 Hz often appear in each vibration data of 0.03 and 0.05, at the vibration frequency range value of 0.08 none appear. This shows the results that the ideal rotor clearance value is at a distance of 0.08 because there is no frequency graph of 1x, 2x, and 3x rpm at each point and axis on the end housing of the screw rotor compressor