Sampling Rate Research Articles

MRI-compatible and sensorless haptic feedback for cable-driven medical robotics to perform teleoperated needle-based interventions.

Surgical robotics have demonstrated their significance in assisting physicians during minimally invasive surgery. Especially, the integration of haptic and tactile feedback technologies can enhance the surgeon's performance and overall patient outcomes. However, the current state-of-the-art lacks such interaction feedback opportunities, especially in robotic-assisted interventional magnetic resonance imaging (iMRI), which is gaining importance in clinical practice, specifically for percutaneous needle punctures. The cable-driven 'Micropositioning Robotics for Image-Guided Surgery' (µRIGS) system utilized the back-electromotive force effect of the stepper motor load to measure cable tensile forces without external sensors, employing the TMC5160 motor driver. The aim was to generate a sensorless haptic feedback (SHF) for remote needle advancement, incorporating collision detection and homing capabilities for internal automation processes. Three different phantoms capable of mimicking soft tissue were used to evaluate the difference in force feedback between manual needle puncture and the SHF, both technically and in terms of user experience. The SHF achieved a sampling rate of 800Hz and a mean force resolution of 0.26 ± 0.22N, primarily dependent on motor current and rotation speed, with a mean maximum force of 15N. In most cases, the SHF data aligned with the intended phantom-related force progression. The evaluation of the user study demonstrated no significant differences between the SHF technology and manual puncturing. The presented SHF of the µRIGS system introduced a novel MR-compatible technique to bridge the gap between medical robotics and interaction during real-time needle-based interventions.

Fine-Needle Aspiration versus the CytoCore® Motorized Rotating Needle Device for Thyroid Nodule Biopsies: A Retrospective Cohort Study

Introduction: Recently, an FDA cleared motorized fine-needle aspiration device (CytoCore®, Praxis Medical) has become available which is designed to reduce sample variability by enabling more consistent sampling due to the rotational drilling action of the device in combination with the standard in and out motion used to access the thyroid nodule with a needle. The rotation of the needle permits the ability to collect a higher quantity of intact cellular material, which is optimal for determining adequacy and, ultimately, for making a diagnosis. The present study compares the diagnostic performance of a motorized fine needle aspiration (FNA) device to a historical cohort of patients biopsied using ultrasound-guided fine needle aspiration (US-FNA). Methods: Data from 120 patients with thyroid nodules biopsied using a motorized FNA device was retrospectively analyzed. Patient demographics, lesion characteristics, number of passes, Bethesda category, and cellularity scores were compared to a historical control cohort of 100 patients who underwent US-FNA. Nondiagnostic and indeterminate samples rates for motorized FNA were separately compared to literature controls. Results: A significantly reduced median number of passes were required with motorized FNA compared to US-FNA (1.48 ± 0.62 vs. 2.64 ± 1.63, p < 0.001). Adequate samples were obtained after the first pass for 58% of biopsies with motorized FNA compared to 11% with US-FNA. The cumulative percentage of adequate samples increased to 98% after two passes for motorized FNA versus 58% for the US-FNA group. The mean cellularity score was also significantly greater for motorized FNA (3.42 ± 0.63 vs. 1.9 ± 0.59; p < 0.001). A determinant diagnosis was possible for a greater number of samples in the motorized FNA group compared to the control group (91.6% vs. 78%; p = 0.05). The motorized FNA also had a lower nondiagnostic rate compared to US-FNA (2.0% vs. 10%) and a lower indeterminate rate compared to published rates associated with the use of FNA (8.3% vs. 20.0%; p = 0.05). Conclusion: The motorized FNA device required less passes to obtain an adequate biopsy than US-FNA. Its use is also associated with obtaining samples with a higher cellularity and lower nondiagnostic and indeterminate sample rates.

Evaluation of mycobacterial microscopy and culture results from clinical samples: A five-year analysis.

Ehrlich-Ziehl-Neelsen (EZN) staining and culture methods are often used to diagnose tuberculosis. This study aimed to determine the acidfast bacteria (AFS) positivity rates in various clinical samples sent to our laboratory over five years and the growth and resistance rates in two different (solid and liquid) cultures and compare them with the data from Türkiye and the world. A total of 62.456 clinic samples were accepted in the microbiology laboratory between 2019 and 2024. The mycobacterial culture was performed by searching for acid-resistant bacilli microscopically and parallel inoculation media [solid Löwenstein-Jensen (L-J) and MGIT 960 liquid]. Those growing in the MGIT 960 system were identified using BD MGIT TBC Identification test kits that detect the MPT64 antigen. AFS and MPT64 antigenpositive samples were identified as Mycobacterium tuberculosis complex (MTBC) while AFS-positive samples and MPT64 antigen-negative results were classified as non-tuberculous mycobacteria (NTM). Drug susceptibility testing was performed with the BACTEC MGIT 960 SIRE kit. Susceptibility to NTM samples was not performed. Out of a total of 120.829 samples, 95.101 were lung samples and 25.728 were extrapulmonary samples. AFS positivity was detected in 2961 (2.4%) samples. MTBC grew in 6854 (5.6%) samples, and NTM grew in 1506 (1.24%) samples. Contamination was detected in 7171 (5.9%) media. Two thousand one hundred and sixty-nine susceptibility tests were performed. Considering antibiotic resistance rates, isoniazid resistance was detected in 154 (7%), rifampicin resistance in 140 (6.4%), ethambutol resistance in 18 (0.8%), and streptomycin resistance in 120 (0.5%) samples. All four-drug resistance was observed in 91 (4.1%) samples. AFP positivity and resistance rates for rifampicin have decreased significantly, while there have been no significant changes in NTM rates over the years. When our data was determined, the sensitivity of microscopy was low. It is understood that mycobacterial culture and microscopy must be evaluated together to exclude tuberculosis infection. The high mycobacterial culture positivity rate, which is 5.6%, is due to the high number of follow-up patients and new referrals. It is seen that the change in sensitivity rates is due to the period of the COVID-19 epidemic, and it is similar to World Health Organization (WHO) data.

Enhanced photodegradation performance of Zn-BiPO4 on RhB and TC under the synergistic effect of oxygen vacancies and built-in electric field

In this work, Zn2+ ions doped BiPO4 photocatalyst was successfully prepared by one-step hydrothermal method for the first time. The degradation rates of the optimal sample on RhB and TC solution reached 91.5 % and 81.25 % under simulated sunlight exposure for 3 and 5 hours, respectively, while pure BiPO4 only reached 59.22 % and 49.4 %. The improved photocatalytic performance was due to the synergistic effect of oxygen vacancies and built-in electric field. The EPR test demonstrated the generation of more oxygen vacancies in the optimal sample. Additionally, the electrochemical experiments showed that the photocurrent intensity of the optimal sample was about 3 times than that of pure BiPO4, indicating an enhanced carrier separation and transfer ability after doping Zn2+ ions. Through density functional theory (DFT), the incorporated Zn2+ ions caused the contraction of crystal structure and the redistribution of charges, leading to a built-in electric field. As few studies pay attention to the relationship between oxygen vacancies and the built-in electric field. This study has enlightening significance for the construction of photocatalysts with oxygen vacancies and built-in electric field through doping engineering.

The Effect of Nickel Content and Mechanical Activation on Combustion in the 5Ti + 3Si + &lt;i&gt;х&lt;/i&gt;Ni System

Intermetallic alloys were synthesized in the 5Ti + 3Si + xNi system by the method of self-propagating high-temperature synthesis (SHS) and mechanosynthesis. The influence of nickel content on the morphology, size and yield of composite particles after mechanical activation (MA) of mixtures was studied. The dependences of the maximum temperatures and combustion rates, phase composition, morphology and elongation of synthesis products on the nickel content for the initial and MA mixtures are studied. Under the conditions of the experiments conducted in this work, combustion process was able to realize and at the same time the samples burned completely at a nickel content of 10 to 60 wt.% in the 5Ti + 3Si + xNi system. After MA, the samples from the 5Ti + 3Si mixture burned to the end, and during the activation of the 5Ti + 3Si + 40% Ni mixture, mechanochemical synthesis occurred. With increasing nickel content combustion temperature decreases, and combustion velocity behaves nonmonotonically, increases the size of composite particles and decreases the yield of the mixture after MA. MA practically did not affect the maximum combustion temperatures of mixtures of 5Ti + 3Si + xNi. A multiple (from 0.7 to 2.9 cm/s) increase in the burning rate of samples from MA mixtures with an increase in the Ni content from 20 to 30 wt. % was recorded. An increase in the nickel content leads to an increase in the content of triple phases and the amount of melt in the synthesis products of mixtures of 5Ti + 3Si + xNi. Shrinkage of product samples increases with increasing nickel content in the initial mixtures. After MA, the shrinkage of the product samples is replaced by their growth. Explanations of the observed dependencies are proposed.

Citizen science: Development of a low-cost magnetometer system for a coordinated space weather monitoring

As part of Ham Radio Science Citizen Investigation (HamSCI) Personal Space Weather Station (PSWS) project, a low-cost, commercial off-the-shelf magnetometer has been developed to provide quantitative and qualitative measurements of the geospace environment from the ground for both scientific and operational purposes at a cost that will allow for crowd-sourced data contributions. The PSWS magnetometers employ a magneto-inductive sensor technology to record three-axis magnetic field variations with a field resolution of ∼3 nT at a 1 Hz sample rate. The measurement range of the sensor is ±1.1×106 nT) and is valid over a temperature range of −40 °C to +85 °C. Data from the PSWS network will combine these magnetometer measurements with high frequency (HF, 3–30 MHz) radio observations to monitor large-scale current systems and ionospheric disturbances due to drivers from both space and the atmosphere. A densely-spaced magnetometer array, once established, will demonstrate their space weather monitoring capability to an unprecedented spatial extent. Magnetic field data obtained by the magnetometers installed at various locations in the US are presented and compared with the existing magnetometers nearby, demonstrating that the performance is very adequate for scientific investigations.

Multi‐image encryption and authentication using computational ghost imaging and singular value decomposition

AbstractThe non‐local imaging characteristics of computational ghost imaging (CGI) make it widely used in the field of information security. However, the information processing method based on traditional CGI has some problems in the process of multi‐image encryption, such as low efficiency and unsatisfactory reconstruction quality. Therefore, this article proposes to use wavelet transform and singular value decomposition technology to help computational ghost imaging to efficiently complete the coding and encryption of multiple images, and use alternating direction multiplier method (ADMM) in the process of image restoration to carry out high‐quality reconstruction of the original encrypted image. The results show that the scheme has good reconstruction effect, strong security and robustness even at a low sampling rate. It provides some reference for digital watermarking technology and cryptography.

Photonic probabilistic machine learning using quantum vacuum noise

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element – a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~1 Gbps and energy consumption of ~5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.

Optimizing Energy Harvesting in Wireless Body Area Networks: A Deep Reinforcement Learning Approach to Dynamic Sampling

In energy-harvesting wireless body area networks (EH-WBAN), the self-sustainability of body sensor nodes without compromising the service quality criteria is very important. In remote vital signs monitoring applications, the sampling rate of body nodes in each period should be determined to achieve this goal. There are two fundamental challenges in determining the sampling rate: 1) In EH-WBAN, the rate of the harvestable energy is time-dependent and unpredictable. 2) The vital signs of the patient have different change rates relevant to the time. Therefore, a technique is required that can learn the changes of the harvestable energy and the change rates of vital signs simultaneously and specify the proper sampling rate for BNs. The reinforcement learning algorithms are among the efficient solutions for this problem because they are capable of learning environmental uncertainties. Previous RL-based methods for determining the BN's sampling rate have three fundamental problems: 1) focus on the optimization of the transmission energy and ignore the sensing energy, 2) only affect one of the two aspects of energy or sensed data in determining the sampling rate and 3) discretization of the problem space does not ensure the determination of the optimal sampling rate. Therefore, this paper proposes a method named “deep reinforcement learning-based dynamic sampling” (DRDS) which first formulates the sampling rate determination problem as a Markov decision process (MDP) and proposes a deep deterministic policy gradient algorithm (DDPG) to solve it. The proposed method considers both energy and data variability aspects in determining the sampling rate. Two parameters, the super-capacitor’s voltage level, and ambient light intensity, are considered for the energy aspect; for the data variability aspect, the long short-term memory (LSTM) algorithm is developed to predict the change rate of data in the next round. Simulations indicate that by preserving the data integrity, the proposed method can decrease the sampling rate and unnecessary data transmission by about 49.95% and 89.7%, respectively, compared with state-of-the-art methods, and allow the sensor to achieve self-sustainability.

Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.

Enhanced ABLO-OFDM performance by using composite multi-mode index modulation in IM/DD systems

In intensity modulation direct detection (IM/DD) systems, an adaptively biased layered optical orthogonal frequency division multiplexing (ABLO-OFDM) scheme is proposed based on composite multi-mode index modulation (CMM-IM). The ABLO-OFDM has a simple structure, easy demodulation, low complexity, and effectively reduces the peak-to-average power ratio (PAPR). Since CMM-IM extends the index to the constellation domain, it increases the number of index bits to further improve the spectral efficiency (SE). By dynamically adjusting the number of activated subcarriers and the number of layers, various signal power can be achieved to enhance system flexibility and reliability. The system performance of four schemes is compared and analyzed in IM/DD systems. The result shows that, for the same SE, CMM-ABLO (4,3) achieves a 3 dB receiver sensitivity gain compared with CMM-ABLO (8,2). When L is 3, four schemes can transmit over about 50 km single-mode fiber under the soft decision forward error correction (SD-FEC) threshold. When L is 1 and the sample rate is less than 20 GSa/s, both CMM-ABLO (4,2) and CMM-ABLO (4,3) can achieve error-free transmission. Moreover, as the number of layers increases, PAPR decreases and it reaches a maximum reduction of 9.6 dB.

Mid‐Infrared Single‐Photon Compressive Spectroscopy

AbstractSensitive mid‐infrared (MIR) spectroscopy plays an indispensable role in various photon‐starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi‐pixel arrays in parallel spectral acquisition. Here, an ultra‐sensitive MIR single‐pixel spectrometer is devised and implemented, which relies on high‐fidelity spectral upconversion and wavelength‐encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 µm is nonlinearly converted to the NIR band via synchronous chirped‐pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single‐element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 under an illumination flux down to 0.01 photons nm–1 pulse–1. Moreover, faithful reconstructions at sub‐Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which leads to a 95% reduction in data acquisition time. The presented single‐pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single‐photon level.

Domestic and Family Violence Screening and Response: A Prospective, Cross-Sectional, Mixed Methods Survey in Private Mental Health Clients.

Most domestic and family violence (DFV) research has focused on establishing prevalence and screening rates in public health and community samples. This study sought to address a gap in the literature by evaluating DFV screening and response practices in a private mental healthcare inpatient service and determining if clients of the service had unmet DFV needs. A prospective, convenience sample, mixed methods, cross-sectional survey of adult inpatient mental health consumers was employed. Sixty-two participants completed the Royal Melbourne Hospital Patient Family Violence Survey. Quantitative Likert-type and categorical responses were collated and analysed descriptively (count and percentage). Free-text responses were analysed using qualitative description within a content analysis framework. Sixty-five percent of participants had been screened for at least one DFV issue, on at least one occasion, with 35% not being screened, to their recall. Twenty-three percent reported disclosing DFV concerns, 82% felt very supported by the clinician's response to their disclosure, and 86% were provided with information they found helpful. Unmet needs were identified in 13% of participants, who had wanted to disclose DFV concerns but not feel comfortable to do so. No unscreened respondents disclosed DFV concerns, highlighting the need to uphold best practice guidelines for direct enquiry. Most disclosing clients were positive about the support they received. Indicated areas for improvement were screening rates, active follow-up, increasing psychology support levels and safety planning.

Dynamic three-dimensional reconstruction with phase shift coding division multiplexing

Fringe projection profilometry with temporal phase unwrapping (FPP-TPU) has been gotten more and more attention in reconstructing the object with complex surfaces. However, a general contradiction in the existing FPP-TPUs is that more auxiliary coded patterns may limit sampling rate in dynamic three-dimensional (3D) measurement. In this paper, inheriting the single-frame auxiliary pattern of the phase-shifting temporal phase unwrapping (PS-TPU), an improved phase shift coding temporal phase unwrapping with higher robustness is proposed. And new projection stratagem with phase shift coding division multiplexing (PSC-DM) is also proposed to enhance the reconstructing rate in dynamic 3D scene. Firstly, six codewords instead of the traditional eight codewords are embedded into the single-frame auxiliary coding pattern, which guarantees the decoding robustness and provides a faster decoding process with single-step direction unifying. Then, the new projection sequence stratagem is designed with every two sets of 3-step phase-shifting patterns projection followed by the single-frame phase-shifting coding pattern projection. During phase unwrapping, every adjacent three frames of phase-shifting pattern sequentially resolving a wrapped phase and a highly robust 3D reconstruction can be achieved by using the closest phase-shifting coding pattern so as to achieve phase-shifting coding division multiplexing. It can obtain higher reconstructing rate. In addition, a phase shift segmentation is used to correct mismatched errors of fringe order. The experimental results demonstrate that the proposed method can increase the reconstructing rate from the rate of 30 fps in traditional PS-TPU method to 68.57 fps at the digital light projector (DLP) projection rate of 120 fps, which shows its potential prospective in dynamic 3D shape reconstruction.

CLARA: A cluster-based node correlation for sampling rate adaptation and fault tolerance in sensor networks

Recently, wireless sensor networks (WSNs) have been proven as an efficient and low-cost solution for monitoring various kind of applications. However, the massive amount of data collected and transmitted by the sensor nodes, which are mostly redundant, will quickly consume their limited battery power, which is sometimes difficult to replace or recharge. Although the huge efforts made by researchers to solve such problem, most of the proposed techniques suffer from their accuracy and their complexity, which is not suitable for limited-resources sensors. Therefore, designing new data reduction techniques to reduce the raw data collected in such networks is becoming essential to increase their lifetime. In this paper, we propose a CLuster-based node correlation for sAmpling Rate adaptation and fAult tolerance, abbreviated CLARA, mechanism dedicated to periodic sensor network applications. Mainly, CLARA works on two stages: node correlation and fault tolerance. The first stage introduces a data clustering method that aims to search the correlation among neighboring nodes. Then, it accordingly adapts their sensing frequencies in a way to reduce the amount of data collected in such networks while preserving the information integrity at the sink. In the second stage, a fault tolerance model is proposed that allows the sink to regenerate the raw sensor data based on two methods: moving average (MA) and exponential smoothing (ES). We demonstrated the efficiency of our technique through both simulations and experiments. The best obtained results show that the first stage can reduce the sensor sampling rate, and accordingly the sensor energy, up to 64% while the second stage can accurately regenerate the raw data with an error loss less than 0.15.

The efficiency of detecting seabird behaviour from movement patterns: the effect of sampling frequency on inferring movement metrics in Procellariiformes

BackgroundRecent technological advances have resulted in low-cost GPS loggers that are small enough to be used on a range of seabirds, producing accurate location estimates (± 5 m) at sampling intervals as low as 1 s. However, tradeoffs between battery life and sampling frequency result in studies using GPS loggers on flying seabirds yielding locational data at a wide range of sampling intervals. Metrics derived from these data are known to be scale-sensitive, but quantification of these errors is rarely available. Very frequent sampling, coupled with limited movement, can result in measurement error, overestimating movement, but a much more pervasive problem results from sampling at long intervals, which grossly underestimates path lengths.MethodsWe use fine-scale (1 Hz) GPS data from a range of albatrosses and petrels to study the effect of sampling interval on metrics derived from the data. The GPS paths were sub-sampled at increasing intervals to show the effect on path length (i.e. ground speed), turning angles, total distance travelled, as well as inferred behavioural states.ResultsWe show that distances (and per implication ground speeds) are overestimated (4% on average, but up to 20%) at the shortest sampling intervals (1–5 s) and underestimated at longer intervals. The latter bias is greater for more sinuous flights (underestimated by on average 40% when sampling > 1-min intervals) as opposed to straight flight (11%). Although sample sizes were modest, the effect of the bias seemingly varied with species, where species with more sinuous flight modes had larger bias. Sampling intervals also played a large role when inferring behavioural states from path length and turning angles.ConclusionsLocation estimates from low-cost GPS loggers are appropriate to study the large-scale movements of seabirds when using coarse sampling intervals, but actual flight distances are underestimated. When inferring behavioural states from path lengths and turning angles, moderate sampling intervals (10–30 min) may provide more stable models, but the accuracy of the inferred behavioural states will depend on the time period associated with specific behaviours. Sampling rates have to be considered when comparing behaviours derived using varying sampling intervals and the use of bias-informed analyses are encouraged.

An energy-efficient 16 MS/s 10-bit SAR ADC with MSB-block switching scheme

An energy-efficient MSB-block switching scheme without common-mode voltage variation for successive approximation register (SAR) analog-to-digital converters (ADCs) is proposed. Benefit from a pair of extra switches embedded in the capacitive digital-to-analog converter (CDAC), the proposed MSB-block switching scheme can reduce switching energy without further reducing the capacitor unit. The proposed switching scheme can achieve 93.8 % and 49.9 % savings in switching energy compared with the conventional switching scheme and the Vcm-based switching scheme, and the simulated differential-nonlinearity (DNL) and integrated-nonlinearity (INL) are 0.160 and 0.156LSB, respectively. The proposed switching scheme is verified in a 1.2-V 10-bit 16 MS/s SAR ADC in 65 nm CMOS technology. At maximum sampling rate, the proposed SAR ADC achieves an effective number of bits (ENOB) of 9.50 and a power consumption of 103.5μW, leading to a Figure of Merit of 8.93 fJ/Conversion-step.

A modified sampling method for the precise detection of prostate cancer tissues using a three-dimensional stereotaxic location technique.

The rapid and accurate acquisition of prostate cancer pathological tissue is critical to prostate cancer research but has traditionally proven challenging. However, the gradual application of three-dimensional (3D) modeling in medical practice has overcome many of the related limitations. This cohort study aimed to compare the difference between a 3D stereotaxic sampling method and traditional cognitive sampling method to clarify the factors affecting sampling. An analysis of 111 men who received radical prostatectomy for prostate cancer at The First Affiliated Hospital of Soochow University between November 2020 and April 2022 was conducted. The positive rate of the cognitive sampling method and the 3D stereotaxic sampling method and their respective influencing factors, such as age, body mass index (BMI), prostate-specific antigen (PSA), PSA density (PSAD), International Society of Urological Pathology (ISUP) grade, tumor volume, number of positive needles from perineal puncture, clinical T stage, and tumor image location, were compared and analyzed, and a cohort study was conducted. Among the 111 patients, there were 57 cases of cognitive sampling and 54 cases of 3D stereotaxic sampling. In this study, the positive rate of cognitive sampling was 29.82% (17/57,), and the positive rate of 3D stereotaxic sampling was 61.11% (33/54), with the positive rate of 3D stereotaxic sampling being significantly higher than that of cognitive sampling (P=0.001). In cognitive sampling, tumor volume [odds ratio (OR) =1.10; 95% confidence interval (CI): 1.02-1.20], number of positive biopsy cores (OR =1.30; 95% CI: 1.06-1.60), Prostate Imaging Report and Data System (PI-RADS) score (OR =5.54; 95% CI: 1.60-19.12), and clinical T stage (OR =2.36; 95% CI: 1.31-4.25) were identified as influencing factors; in 3D stereotaxic sampling, these influencing factors were eliminated, with ORs of 1.22 (95% CI: 0.78-1.90), 0.88 (95% CI: 0.72-1.09), 1.09 (95% CI: 0.62-1.92), and 1.51 (95% CI: 0.86-2.65), respectively, representing a statistically significant difference (P<0.05). The 3D stereotaxic sampling method can accurately obtain the required prostate cancer tissue from the prostate in vitro within a short time, and the factors affecting the positive rate of sampling can be eliminated.

A 10-bit 70-MS/s SAR ADC with Reference Buffer in 40nm CMOS

Abstract A 10-bit 70-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) with reference buffer is presented in this paper. Segmented capacitive DAC with redundant 2bits is designed to relax the design of reference buffer. A hybrid switching sequence is applied to the ADC to optimize power consumption. The Measurement results Show that the core SAR ADC achieves 48.52 dB SNDR and 56.67 dB SFDR at 70 MS/s sampling rate and Nyquist input frequency, consuming 2.50mW power consumption, achieving a FoM of 163.6 fJ/step. The ADC occupies an active area of 0.017 mm2 in 40nm CMOS technology.

Tribology of Ti2/Cr2 and (TiCr)2AlCx MAX Phase Ceramics

The MAX phase ceramics namely Ti2AlCx, Cr2AlCx, and (TiCr)2AlCx were examined under dry sliding conditions against stainless steel ball at room temperature and 5 N applied normal load using a Ball-on-disk tribometer. At 5 N, 100 m sliding distance and 20 cm/s sliding velocity, Cr2AlCx, and (TiCr)2AlCx ceramic samples exhibited low specific wear rate (3.2 × 10-7 cm3/Nm and 4.2 × 10-7 cm3/Nm), while the coefficients of friction were 0.31 and 0.10, respectively. At 5 N, 100 m and increasing velocity from 5 cm/s to 20 cm/s, Ti2AlCx sample the coefficients of friction decreases from 0.25 to 0.14. The specific wear rate of Ti2AlCx sample was 6.7 × 10-7 cm3/N m at 5 N, 100 m and 5 m/s, and decreases with increasing velocity (3.3 × 10-7 cm3/N m at 20 m/s). The correlations between observed tribological properties and tribo-film characteristics were discussed.