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The paper aims to study the sensor that identifies the maturity of oil palm fruit bunches by using a flat-type inductive concept based on a resonant frequency technique. Conventionally, a human grader is used to inspect the ripeness of the oil palm fresh fruit bunch (FFB) which can be inconsistent and inaccurate. There are various new methods that are proposed with the intention to grade the ripeness of the oil palm FFB, but none has taken the inductive concept. In this study, the resonance frequency of the air coil is investigated. Samples of oil palm FFB are tested with frequencies ranging from 20 Hz to 10 MHz and the results obtained show a linear relationship between the graph of the resonance frequency (MHz) against time (Weeks). It is observed that the resonance frequencies obtained for Week 10 (pre-mature) and Week 18 (mature) are around 8.5 MHz and 9.8 MHz, respectively. These results are compared with the percentage of the moisture content. Hence, the inductive method of the oil palm fruit maturity sensor can be used to detect the change in water content for ripeness detection of the oil palm FFB.

T2*-weighted gradient-echo MRI images at high field (≥7T) have shown rich image contrast within and between brain regions. The source for these contrast variations has been primarily attributed to tissue magnetic susceptibility differences. In this study, the contribution of myelin to both T2* and frequency contrasts is investigated using a mouse model of demyelination based on a cuprizone diet. The demyelinated brains showed significantly increased T2* in white matter and a substantial reduction in gray-white matter frequency contrast, suggesting that myelin is a primary source for these contrasts. Comparison of in-vivo and in-vitro data showed that, although tissue T2* values were reduced by formalin fixation, gray-white matter frequency contrast was relatively unaffected and fixation had a negligible effect on cuprizone-induced changes in T2* and frequency contrasts. [Display omitted] ► Cuprizone-induced demyelination increased WM T2* and reduced GM–WM T2* contrast. ► GM–WM frequency (phase) contrast was also reduced. ► Tissue fixation decreased T2* values but did not affect GM–WM frequency contrast.

This study validated a more exact automated method of determining cardiovascular resonance frequency (RF) against the “stepped” protocol described by Lehrer et al. (Appl Psychophysiol Biofeedback 25(3):177–191, https://doi.org/10.1023/a:1009554825745, 2000; in Foundations of heart rate variability biofeedback: A book of readings, The Association for Applied Psychophysiology and Biofeedback, pp 9–19, 2016). Thirteen participants completed a 15-min RF determination session by each method. The “stepped” protocol assesses HRV in five 3-min stationary windows from 4.5 to 6.5 breaths per minute (bpm), decreasing in 0.5 bpm steps. Multiple criteria, subjectively weighted by the clinician, determines RF. For this study, the proposed method used a sliding window with a fixed rate of change (67.04 ms per breath) at each of 78 breath cycles ranging from 4.25 to 6.75 bpm. Its algorithm analyzes IBI to locate the midpoint of the 1-min region of stable maximum peak-trough variability. RF is quantified from breath duration at that point. The software generates a visual display of superimposed HR and breathing data. Thus, the new method fully automates RF determination. Eleven of the 13 matched pairs fell within the 0.5 bpm resolution of the stepped method. Comparisons of LF power generated by the autoregressive (AR) spectral method showed a strong correlation in LF power production by the stepped and sliding methods (R = 0.751, p = 0.000). The “sliding” pacing protocol was favored by 69% of participants (p < 0.02). The new, fully-automated, method may facilitate both in-person and remote HRV biofeedback training. Software is available open-source.

Evaluation of the initial stability of implants is essential to reduce the number of implant failures of pedicle screws after orthopedic surgeries. Laser resonance frequency analysis (L-RFA) has been recently proposed as a viable diagnostic scheme in this regard. In a previous study, L-RFA was used to demonstrate the diagnosis of implant stability of monoaxial screws with a fixed head. However, polyaxial screws with movable heads are also frequently used in practice. In this paper, we clarify the characteristics of the laser-induced vibrational spectra of polyaxial screws which are required for making L-RFA diagnoses of implant stability. In addition, a novel analysis scheme of a vibrational spectrum using L-RFA based on machine learning is demonstrated and proposed. The proposed machine learning-based diagnosis method demonstrates a highly accurate prediction of implant stability (peak torque) for polyaxial pedicle screws. This achievement will contribute an important analytical method for implant stability diagnosis using L-RFA for implants with moving parts and shapes used in various clinical situations.

With recent advances in scanning probe microscopy (SPM), it is now routine to determine the atomic structure of surfaces and molecules while quantifying the local tip-sample interaction potentials. Such quantitative experiments using noncontact frequency modulation atomic force microscopy is based on the accurate measurement of the resonance frequency shift due to the tip-sample interaction. Here, we experimentally show that the resonance frequency of oscillating probes used for SPM experiments change systematically as a function of oscillation amplitude under typical operating conditions. This change in resonance frequency is not due to tip-sample interactions, but rather due to the cantilever strain or geometric effects and thus the resonance frequency is a function of the oscillation amplitude. Our numerical calculations demonstrate that the amplitude dependence of the resonance frequency is an additional yet overlooked systematic error source that can result in nonnegligible errors in measured interaction potentials and forces. Our experimental results and complementary numerical calculations reveal that the frequency shift due to this amplitude dependence needs to be corrected even for experiments with active oscillation amplitude control to be able to quantify the tip-sample interaction potentials and forces with milli-electron volt and pico-Newton resolutions.

Objectives Nonsteroidal anti‐inflammatory drugs (NSAIDs) are often prescribed following the placement of dental implants, but the effects of these drugs on the osseointegration process are poorly understood. We designed a randomized, placebo‐controlled pilot study to quantitatively assess the effect of NSAIDs during early implant osseointegration. Materials and Methods Subjects receiving a maxillary dental implant were randomized to take naproxen or placebo for 7 days after the surgery. Implant osseointegration was quantified using Resonance Frequency Analysis device. Implant‐Stability‐Quotient (ISQ) measurement was performed at the time of surgery and at follow‐up visits 1, 4, and 16 weeks after surgery. Periapical radiographs were taken to measure the marginal bone level. Separately, a questionnaire of NSAIDs usage was provided to subjects presenting with early implant failure. Results After 4 weeks, ISQ values increased modestly ( + 1%) in subjects receiving naproxen whereas subjects receiving placebo had a much larger increase in ISQ value (+41%). We observed 55% more marginal bone loss at 4 weeks, and 52% at 16 weeks in the naproxen group compared to the placebo group. These results were not found to have statistically significant between groups (p ≥ 0.05). These effect sizes and variance were used to conduct a power analysis to determine the necessary sample size for future studies. Furthermore, our separate questionnaire study revealed that 68% of our patients with early failed dental implants reported a history of NSAIDs usage after the surgery. Conclusion In conclusion, this pilot study provides effect sizes and sample size estimates for future studies to definitively determine recommendations regarding NSAID usage following dental implant surgery. Nonetheless, our study did not observe any statistically significant differences in ISQ value or marginal bone loss after up to 16 weeks of follow‐up between subjects from naproxen and placebo groups.

Background and Objectives: The aim of this study was to clinically evaluate the primary and secondary stability of dental implants with different macrodesigns using resonance frequency analysis and to determine whether implant design and length influence implant stability. Materials and methods: This study included 48 healthy patients receiving dental implants, and a pre-implant planning protocol was used, which involved detailed bone analysis, clinical examinations, and Cone beam computed tomography (CBCT) analysis. The implants were of various types and dimensions (Alpha-Bio Tec (Israel), DFI, SPI, and NEO), and the surgical procedures were performed using standard methods. Implant stability was measured using resonance frequency analysis (RFA) immediately after placement and after 3, 6, and 12 months. The total number of implants placed in all patients was 96. Results: The average primary stability value for 10 mm SPI implants placed in the maxilla was 68.2 ± 1.7 Implant Stability Quotient (ISQ) units, while for 10 mm NEO implants, it was 74.0 ± 0.9. The average primary stability value for a 10 mm DFI implant placed in the mandible was 72.8 ± 1.2 ISQ, while for a 10 mm NEO implant placed in the mandible, it was 76.3 ± 0.8 ISQ. Based on the Friedman ANOVA test, the differences in the stability measurements for the 10 mm and 11.5 mm SPI implants and for the 10 mm and 11.5 mm NEO implants in the maxilla on day 0 and after 3, 6, and 12 months were significant at p < 0.05. Similarly, based on the Friedman ANOVA test, the differences in the stability measurements for the 10 mm and 11.5 mm DFI implants and for the 10 mm and 11.5 mm NEO implants in the mandible on day 0 and after 3, 6, and 12 months were significant at p < 0.05 (p = 0.00000). Conclusions: Universal tapered implants of the NEO type stood out as the optimal choice, as they provided statistically significantly higher primary stability in both soft and hard bone types compared to other implants. The implant length did not significantly affect this stability.

We propose a sub-1 Hz resonance frequency MEMS resonator that can be used for seismometers. The low resonance frequency is achieved by an electrically tunable spring with an ultra-small spring constant. Generally, it is difficult to electrically fine-tune the resonance frequency at a near-zero spring constant because the frequency shift per voltage will diverge at the limit of zero spring constant. To circumvent this issue, we propose a multi-step electrical tuning method. We show by simulations that the resonance frequency can be tuned by 0.008 Hz/mV even in the sub-1 Hz region. The small spring constant, however, reduces the shock robustness and dynamic range of the seismometer. To prevent this, we employ a force-balanced method in which the mass displacement is nulled by the feedback force. We show that the displacement can be obtained from the voltage that generates the feedback force.

To prevent pedicle screw implant failure, a diagnostic technique that allows surgeons to evaluate implant stability easily, quickly, and quantitatively in clinical orthopedic situations is required. This study aimed to predict the insertion torque equivalent to laboratory-level evaluation accuracy. This serves as an index of the implant stability of pedicle screws placed in cadaveric bone, which relies on laser resonance frequency analyses (L-RFA) when irradiating with two types of lasers. The machine learning analysis was optimized using a dataset with artificial bone as teaching data. In this analysis, many explanatory variables extracted from the laser-induced vibration spectra obtained during an analysis/RFA evaluation were predicted by selecting important variables using the least absolute shrinkage and selection operator and performing a non-linear approximation using support vector regression. It was found that combining both artificial and cadaveric bone data with the bone densities as teaching data dramatically improved the determination coefficient from R2 = −0.144 to R2 = 0.858 as the prediction accuracy and reduced the influence of differences between artificial and cadaveric bones. This technology will contribute to the development of preventive diagnostic technologies that can be used during surgery, which is necessary in order to further advance treatment technologies.

Using current battery technology the life-time of a leadless pacemaker is approximately 6-10 years, with a large portion of the pacemaker occupied by the battery. This paper investigates the possibility to use a MEMS piezoelectric harvester as a complementary energy source in leadless pacemakers. The challenge is to combine the low resonance frequency required to harvest energy from a heartbeat with the small volume of 20×4×3 mm3 available, with the corresponding harvester displacement restricted to 2 mm. Due to the displacement restriction the selected structure was a double clamped bridge in order to reduce the mass displacement, with various meander-type designs simulated to reduce resonance frequency. To further reduce resonance frequency large proof-masses of tungsten were attached by gluing. Two types of tungsten proof-masses were added to four different harvesters, 16.4 mg and 16.6 mg on sample 1 and 2 and 502 mg and 492 mg proof-mass on sample 3 and 4. The structures have 2 µm patterned PZT (deposited by sol-gel technique) and Pt metal electrodes for d31 mode harvesting. The power output measured from one of the two PZT/electrodes was 0.13 nW with 50 µm deflection at 100 k Ω optimal load resistance and 9.1 mVpp at 232 Hz.