Exploring the Feasibility of Estimating Intraocular Pressure Using Vibrational Response of the Eye: A Methodological Approach.

This study addresses the limitations of current tonometry techniques by exploring vibroacoustic properties for estimating intraocular pressure (IOP), a key diagnostic parameter for monitoring glaucoma-a significant risk factor for vision loss. Utilizing vivo porcine eyeballs, we investigated the relationship between IOP and the nonlinear vibration transfer function ratio (NVTFR). Through applying varying vibration levels and analyzing responses with transfer function analysis and univariate regression, we identified a strong negative correlation between NVTFR and IOP, evidenced by a Pearson correlation coefficient of -0.8111 and significant results from generalized linear model (GLM) regression (p-value < 0.001). These findings indicate the potential of NVTFR as a vital indicator of IOP changes. Our study highlights the feasibility of using vibroacoustic properties, specifically NVTFR, to measure IOP. While further refinement is necessary for in vivo application, this approach opens new possibilities for non-invasive and patient-friendly IOP monitoring, potentially enhancing ophthalmology diagnostic techniques and providing a foundation for future research and development in this critical area.

Estimation of Elbow Wall Thinning Using Ensemble-Averaged Mel-Spectrogram with ResNet-like Architecture.

An elbow wall thinning diagnosis method by highlighting the stationary characteristics of the operating loop is proposed. The accelerations of curved pipe surfaces were measured in a closed test loop operating at a constant pump rpm, combined with curved pipe specimens with artificial wall thinning. The vibration characteristics of wall-thinned elbows were extracted by using a mel-spectrogram in which modal characteristic variation shifting can be expressed. To reduce the deviation of the model's prediction values, the ensemble mean value of the mel-spectrogram was used to emphasize stationary signals and reduce noise signals. A convolutional neural network (CNN) regression model with residual blocks was proposed and showed improved performance compared to the models without the residual block. The proposed regression model predicted the thinning thickness of the elbow excluded in training dataset.

Chatter Monitoring of Machining Center Using Head Stock Structural Vibration Analyzed with a 1D Convolutional Neural Network.

Real-time chatter detection is crucial for the milling process to maintain the workpiece surface quality and minimize the generation of defective parts. In this study, we propose a new methodology based on the measurement of machine head stock structural vibration. A short-pass lifter was applied to the cepstrum to effectively remove components resulting from spindle rotations and to extract structural vibration modal components of the machine. The vibration modal components include information about the wave propagation from the cutter impact to the head stock. The force excitation from the interactions between the cutter and workpiece induces structural vibrations of the head stock. The vibration magnitude for the rigid body modes was smaller in the chatter state compared to that in the stable state. The opposite variation was observed for the bending modes. The liftered spectrum was used to obtain this dependence of vibration on the cutting states. The one-dimensional convolutional neural network extracted the required features from the liftered spectrum for pattern recognition. The classified features allowed demarcation between the stable and chatter states. The chatter detection efficiency was demonstrated by application to the machining process using different cutting parameters. The classification performance of the proposed method was verified with comparison between different classifiers.

Ultrasonic assessment of osseointegration phenomena at the bone-implant interface using convolutional neural network.

Although endosseous implants are widely used in the clinic, failures still occur and their clinical performance depends on the quality of osseointegration phenomena at the bone-implant interface (BII), which are given by bone ingrowth around the BII. The difficulties in ensuring clinical reliability come from the complex nature of this interphase related to the implant surface roughness and the presence of a soft tissue layer (non-mineralized bone tissue) at the BII. The aim of the present study is to develop a method to assess the soft tissue thickness at the BII based on the analysis of its ultrasonic response using a simulation based-convolution neural network (CNN). A large-annotated dataset was constructed using a two-dimensional finite element model in the frequency domain considering a sinusoidal description of the BII. The proposed network was trained by the synthesized ultrasound responses and was validated by a separate dataset from the training process. The linear correlation between actual and estimated soft tissue thickness shows excellent R2 values equal to 99.52% and 99.65% and a narrow limit of agreement corresponding to [ -2.56, 4.32 µm] and [ -15.75, 30.35 µm] of microscopic and macroscopic roughness, respectively, supporting the reliability of the proposed assessment of osseointegration phenomena.

Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures.

(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI.

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.

Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM.

(1) Background: This study is aimed at the development of a precise and inexpensive device for flow information measurement for external flow. This novel flowmeter uses an LSTM (long short-term memory) neural network algorithm to analyze the vibration responses of the gauge plate. (2) Methods: A signal processing method using an LSTM neural network is proposed for the development of mass flow rate estimation by sensing the vibration responses of a gauge plate. An FFT (fast Fourier transform) and an STFT (short-time Fourier transform) were used to analyze the vibration characteristics of the gauge plate depending on the mass flow rate. For precise measurements, the vibration level and roughness were computed and used as input features. The actual mass flow rate measured by using a weight transducer was employed as the output features for the LSTM prediction model. (3) Results: The estimated flow rate matched the actual measured mass flow rate very closely. The deviations in measurements for the total mass flow were less than 6%. (4) Conclusions: The estimation of the mass flow rate for external flow through the proposed flowmeter by use of vibration responses analyzed by the LSTM neural network was proposed and verified.

Dependence between the vibration characteristics of the proton exchange membrane fuel cell and the stack structural feature.

The purpose of this study was to investigate dynamic characteristics of a proton exchange membrane fuel cell (PEMFC). Fuel cell mounted on a vehicle is excited by road irregularities in operation conditions. To minimize performance degradation, proper vibration isolation after considering vibration properties is required. The vibration modal characteristics of PEMFC was measured by impact test to determine a dominant mode which affects fuel cell durability. The frequency range of the principal responses was identified by comparing the acceleration and interior noise in a fuel cell vehicle. At the resonant frequencies, the mode shapes of the PEMFC were analyzed. The measurement procedures to determine effective dynamic properties of a stack structure were proposed. Structural features of the stacked cell were considered when beam type specimens were fabricated. As the bending and torsional vibrations were applied to the specimens, the vibration responses were measured. The frequency dependent dynamic properties were obtained from the acceleration responses. The measured dynamic properties were compared for different beam constructions. Consequently, influence of structural features on the vibration modes was identified.

DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications.

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

Effect of the static compressive load on vibration propagation in multistory buildings and resulting heavyweight floor impact sounds.

Experiments were performed to identify the mechanism of heavyweight floor impact sound transmission through floors in a high-rise apartment building. Vibration and sound levels on each floor of the multistory building were measured. The vibration generated at a given floor was transferred to multiple adjacent floors with decreasing amplitudes proportional to the distance from the excited floor. This vibration transfer introduced significant sound transmissions. The structural static load varied depending on the floor location due to differences in the weight of the structure above the floor, especially for wall construction buildings. The static load at the wall of the bottom floor was the largest among the different floors. The influence of this static load on the impact sound generation was investigated through tests in the actual building and the scale model, respectively. The results were numerically analyzed using the spectral element method. With the increasing static load, the resonance frequencies of the floor increased due to the change in the vibration modes of the structure. The modulated sound generation from the floor vibrations transmitted to multiple layers with larger magnitudes due to this static load.

Determination of Fluid Density and Viscosity by Analyzing Flexural Wave Propagations on the Vibrating Micro-Cantilever.

The determination of fluid density and viscosity using most cantilever-based sensors is based on changes in resonant frequency and peak width. Here, we present a wave propagation analysis using piezoelectrically excited micro-cantilevers under distributed fluid loading. The standing wave shapes of microscale-thickness cantilevers partially immersed in liquids (water, 25% glycerol, and acetone), and nanoscale-thickness microfabricated cantilevers fully immersed in gases (air at three different pressures, carbon dioxide, and nitrogen) were investigated to identify the effects of fluid-structure interactions to thus determine the fluid properties. This measurement method was validated by comparing with the known fluid properties, which agreed well with the measurements. The relative differences for the liquids were less than 4.8% for the densities and 3.1% for the viscosities, and those for the gases were less than 6.7% for the densities and 7.3% for the viscosities, showing better agreements in liquids than in gases.

Flexural wave cloaking via embedded cylinders with systematically varying thicknesses.

Simulations of flexural wave cloaking from multiple scattering events that are achieved by embedded cylinders in a thin plate are performed. Minimization of refraction is performed using small surrounding cylinders with varying thickness in radial and angular directions, respectively. The thickness variations render the effective wave speed lower in the radial direction and higher in the angular direction compared to the speed in the surrounding media, which results in the cloaking effect. In order to verify the feasibility of this approach, 15 layers of cylinders are placed around the blocked area. The multiple-scattering method is used to predict wave propagations and to take the interactions between cylinders into account. The effects of the thickness variation on the cloaking performance are analyzed. The results demonstrate that minimal scattering is achieved when the area of interest is surrounded by the thickness-varying cylinders.

α-Klotho levels in girls with central precocious puberty: potential as a diagnostic and monitoring marker.

Background: Recent studies suggest a link between the Klotho protein, sex hormones, and insulin-like growth factor-1 (IGF-1), indicating that α-Klotho levels may rise during puberty, including in central precocious puberty (CPP) cases. This study aimed to explore α-Klotho levels in girls with CPP to assess its potential as a diagnostic and monitoring tool for this condition. Methods: In total, 139 girls, comprising 82 patients diagnosed with CPP and 57 healthy prepubertal controls, were enrolled in this study. From March 2020 to May 2023, we assessed both α-Klotho levels and clinical parameters. α-Klotho concentrations were measured using an α-Klotho ELISA kit. For the girls with CPP, we additionally analyzed samples taken 6 months after GnRH agonist treatment. Results: α-Klotho levels were higher in the CPP group compared with the control (CPP group: 2529 ± 999 ng/mL; control group: 1802 ± 675 pg/mL) (P < 0.001), and its level modest decreased after 6 months of GnRH agonist treatment (2147± 789 pg/mL) (P < 0.001). The association between α-Klotho and IGF-1 SDS, follicular stimulating hormone and baseline luteinizing hormone was assessed by partial correlation after adjusting for age, BMI SDS (r= 0.416, p= <0.001; r= 0.261, p= 0.005; r= 0.278, p= 0.002), respectively. Receiver operating characteristic curve analysis identified an α-Klotho cut-off differentiating CPP from controls, with a cut-off of 1914 pg/mL distinguishing girls with CPP from controls with a sensitivity of 69.5% and specificity of 70.2%; the area under the curve was 0.723. Conclusion: The findings of our study are the first step towards deciphering the role of α-Klotho in puberty induction. With additional data and further research, α-Klotho could potentially be utilized as a significant diagnostic and monitoring tool for CPP.

Measurements of dynamic characteristics of intermediate layer in thin semiconductors.

A vibration test method for investigating the dynamic characteristics of thin multi-layered semiconductor wafers was proposed. Flash memory chips whose thickness was varied by grinding the wafers were used as specimens. The specimens composed of silicon, device, and device-protecting layers were excited at the clamped end by using a shaker attached to the clamping device. The vibration of the beam was measured using a laser vibrometer. The wave approach was used to analyze the vibration, from which the complex bending stiffness was determined. A theoretical model to obtain the dynamic characteristics (Young's modulus and the loss factor) of the intermediate device layer by using the measured bending stiffness was investigated. The results were examined and compared with those of a nanoindentation test to verify the accuracy of the model. The proposed method enabled determination of the dynamic properties of the intermediate layer without separation which are essential for understanding the impact response of the wafers during manufacturing process.

Machine learning-enabled autonomous operation for atomic force microscopes.

The use of scientific instruments generally requires prior knowledge and skill on the part of operators, and thus, the obtained results often vary with different operators. The autonomous operation of instruments producing reproducible and reliable results with little or no operator-to-operator variation could be of considerable benefit. Here, we demonstrate the autonomous operation of an atomic force microscope using a machine learning-based object detection technique. The developed atomic force microscope was able to autonomously perform instrument initialization, surface imaging, and image analysis. Two cameras were employed, and a machine-learning algorithm of region-based convolutional neural networks was implemented, to detect and recognize objects of interest and to perform self-calibration, alignment, and operation of each part of the instrument, as well as the analysis of obtained images. Our machine learning-based approach could be generalized to apply to various types of scanning probe microscopes and other scientific instruments.

Wide-field optical coherence tomography deviation map for early glaucoma detection.

BACKGROUND/AIMS: This study aimed to establish a wide-field optical coherence tomography (OCT) deviation map obtained from swept-source OCT (SS-OCT) scans. Moreover, it also aimed to compare the diagnostic ability of this wide-field deviation map with that of the peripapillary and macular deviation maps currently being used for the detection of early glaucoma (EG). METHODS: Four hundred eyes, including 200 healthy eyes and 200 eyes with EG were enrolled in this retrospective observational study. Patients underwent a comprehensive ocular examination, including wide-field SS-OCT (DRI-OCT Triton; Topcon, Tokyo, Japan). The individual wide-field scan was converted into a uniform template using the fovea and optic disc centres as fixed landmarks. Subsequently, the wide-field deviation map was obtained via the comparison between individual wide-field data and a normative wide-field database that had been created by combining images of healthy eyes into a uniform template in a previous study. The ability of the new wide-field deviation map to distinguish between EG and healthy eyes was assessed by comparing it with conventional deviation maps based on the area under the receiver operating characteristic curve (AUC). RESULTS: The wide-field deviation map obtained using the normative wide-field database showed the highest diagnostic ability for the diagnosis of EG (AUC=0.980 and 961 for colour-coded pixels presenting <5% and <1%, respectively) among various deviation maps. Its AUC was significantly superior to that of most conventional deviation maps (p<0.05). The wide-field deviation map demonstrated early structural glaucomatous damage well over a wider area. CONCLUSION: The wide-field SS-OCT deviation map exhibited good performance for distinguishing between eyes with EG and healthy eyes. The visualisation of the wider damaged area on the wide-field deviation map could be useful for the diagnosis of EG in clinical settings.

Accumulation of pre-let-7g and downregulation of mature let-7g with the depletion of EWS.

EWS functions in RNA splicing and transcription by encoding an RNA binding protein, which results in the chromosomal translocation t(11;22)(q24;q12) found in Ewing sarcoma. EWS interacts with the microprocessor complex involving Drosha and DGCR8, which play roles as the cofactors of primary microRNA processing. However, the role of EWS in microRNA biogenesis has not been investigated. Here, we show that endogenous EWS interacts with endogenous Drosha by IP-western blotting. In addition, EWS knockout mouse decreased the expression of Drosha. The depletion of EWS results in the accumulation of precursor let-7g but down-regulates mature let-7g in U2OS cells. Consistently, mature let 7g was suppressed in both Ewing sarcoma cell and primary Ewing sarcoma. Also, expression levels of Dicer and CCND1 (Cyclin D1), which are known target genes of the let-7 family were upregulated. Our findings suggest that EWS mediates generation of mature let-7g from pre-let-7g.

Determination of effective mass density and modulus for resonant metamaterials.

This work presents a method to determine the effective dynamic properties of resonant metamaterials. The longitudinal vibration of a rod with periodically attached oscillators was predicted using wave propagation analysis. The effective mass density and modulus were determined from the transfer function of vibration responses. Predictions of these effective properties compared favorably with laboratory measurements. While the effective mass density showed significant frequency dependent variation near the natural frequency of the oscillators, the elastic modulus was largely unchanged for the setup considered in this study. The effective mass density became complex-numbered when the spring element of the oscillator was viscoelastic. As the real part of the effective mass density became negative, the propagating wavenumber components disappeared, and vibration transmission through the metamaterial was prohibited. The proposed method provides a consistent approach for evaluating the effective parameters of resonant metamaterials using a small number of vibration measurements.