This research's investigation into existing solutions was undertaken to formulate a unique solution, recognizing pivotal contextual conditions. Employing IOTA Tangle, Distributed Ledger Technology (DLT), IPFS protocols, Application Programming Interface (API), Proxy Re-encryption (PRE), and access control, a patient-driven access management system is developed to secure patient medical records and Internet of Things (IoT) medical devices, enabling patients to have complete control over their health records. To exemplify the proposed solution, this research created four prototype applications: the web appointment application, the patient application, the doctor application, and the remote medical IoT device application. The proposed framework promises to fortify healthcare services by delivering immutable, secure, scalable, trustworthy, self-managed, and verifiable patient health records, thereby empowering patients with complete control over their medical information.
The search efficiency of a rapidly exploring random tree (RRT) can be boosted by the strategic introduction of a high-probability goal bias. Strategies employing a fixed step size and a high-probability goal bias will be prone to local optima when dealing with multiple complex obstacles, decreasing search effectiveness. The proposed BPFPS-RRT algorithm, a bidirectional potential field probabilistic step size rapidly exploring random tree, offers a solution for path planning in dual manipulator systems. The approach features a search strategy involving a target angle and a random value for step size determination. The artificial potential field method's introduction entailed a combination of search features, bidirectional goal bias, and the application of greedy path optimization. In simulated scenarios employing the primary manipulator, the proposed algorithm surpasses goal bias RRT, variable step size RRT, and goal bias bidirectional RRT by achieving a 2353%, 1545%, and 4378% reduction in search time, and a 1935%, 1883%, and 2138% decrease in path length, respectively. Furthermore, the proposed algorithm, using the slave manipulator as a prime example, achieves a 671%, 149%, and 4688% reduction in search time, and a respective 1988%, 1939%, and 2083% decrease in path length. For effective path planning of the dual manipulator, the proposed algorithm can be utilized.
The hydrogen sector's expansion into energy generation and storage necessitates the development of more effective methods for detecting hydrogen at trace levels, given the limitations of present optical absorption methods for homonuclear diatomics. Raman scattering stands out as a direct alternative to indirect detection strategies, such as those involving chemically sensitized microdevices, for unequivocally identifying hydrogen's chemical properties. In this task, we evaluated feedback-assisted multipass spontaneous Raman scattering, assessing the accuracy in sensing hydrogen concentrations below two parts per million. A pressure of 0.2 MPa was used for a 10-minute, a 120-minute, and a 720-minute duration measurement, yielding detection limits of 60, 30, and 20 parts per billion, respectively. The lowest probed concentration was 75 parts per billion. An evaluation of diverse signal extraction methods was undertaken, with particular attention to asymmetric multi-peak fitting. This allowed for the resolution of 50 parts per billion concentration steps, thereby determining the concentration of ambient air hydrogen with an uncertainty of 20 parts per billion.
This study investigates the levels of radio-frequency electromagnetic fields (RF-EMF) produced by vehicular communication technology and impacting pedestrians. Our research project comprehensively analyzed exposure levels in children, considering variations in age and gender. This study also compares the exposure levels of children to such technology, contrasted with the exposure levels of an adult participant from our prior research. The exposure scenario entailed a 3D-CAD model of a car fitted with two antennas, both transmitting at 59 GHz, and each powered by 1 watt. Four child models were studied in proximity to the front and back portions of the vehicle. RF-EMF exposure was defined by the Specific Absorption Rate (SAR), encompassing the whole body and the 10-gram mass (SAR10g) of the skin, and the 1-gram mass (SAR1g) of the eyes. Immune evolutionary algorithm The skin of the tallest child's head exhibited the highest SAR10g value, reaching 9 mW/kg. The tallest child experienced a maximum whole-body Specific Absorption Rate (SAR) of 0.18 milliwatts per kilogram. In summary, the findings indicated that children's exposure levels are lower than those of the adult population. The general population's exposure limits as defined by ICNIRP are well exceeded by all the measured SAR values.
A temperature-frequency conversion-based temperature sensor is proposed in this paper, employing 180 nm CMOS technology. The temperature sensor's design includes a proportional-to-absolute temperature current-producing circuit (PTAT), an oscillator (OSC-PTAT) whose frequency depends on temperature, an oscillator (OSC-CON) with a constant frequency, and a divider circuit featuring D flip-flops. High accuracy and high resolution are hallmarks of the sensor, which incorporates a BJT temperature sensing module. Oscillator testing involving the application of PTAT current for capacitor charging and discharging, along with the utilization of voltage average feedback (VAF) for superior frequency stability, was undertaken. The identical dual temperature sensing architecture minimizes the impact of variables, such as fluctuations in power supply voltage, device characteristics, and process deviations. This study reports on the development and testing of a temperature sensor spanning 0-100°C, exhibiting a two-point calibration inaccuracy of ±0.65°C. The sensor's resolution is 0.003°C, with a Figure of Merit (FOM) of 67 pJ/K2, a surface area of 0.059 mm2, and a power consumption of 329 watts.
Spectroscopic microtomography enables the visualization of a microscopic specimen's 4D characteristics, encompassing 3-dimensional structural and 1-dimensional chemical information within a thick sample. We demonstrate spectroscopic microtomography in the short-wave infrared (SWIR) using digital holographic tomography, a technique that allows for the simultaneous acquisition of both absorption coefficient and refractive index. Wavelengths from 1100 to 1650 nanometers can be scanned using a broadband laser integrated with a tunable optical filter. The developed system allows for the measurement of both human hair and sea urchin embryo samples. Senaparib molecular weight Using gold nanoparticles, the resolution for the 307,246 m2 field of view comes to 151 m transverse and 157 m axial. Employing this innovative technique, precise and efficient analyses of microscopic samples exhibiting unique absorption or refractive index characteristics within the SWIR region will be achievable.
The manual wet spraying method, a traditional approach in tunnel lining construction, is characterized by its labor intensity and difficulty in maintaining consistent quality. This research introduces a LiDAR methodology for detecting the amount of tunnel wet spray, intended to enhance efficiency and improve quality standards. Addressing discrepancies in point cloud postures and missing data, the proposed method employs an adaptive point cloud standardization procedure. The Gauss-Newton iteration method is then applied for fitting the segmented Lame curve to the tunnel design axis. Through comparison of the tunnel's actual inner contour line and its design line, this mathematical model of the tunnel section allows for analysis and perception of the wet-sprayed tunnel thickness. Results from experiments indicate the proposed method's successful measurement of tunnel wet spray thickness, presenting key advantages in enabling smart wet spraying processes, refining spray quality, and decreasing labor expenses associated with tunnel lining.
The ever-present challenge of miniaturization and the demand for higher frequencies in quartz crystal sensors places a heightened emphasis on microscopic concerns, including surface roughness, which affect operational performance. This study illuminates the activity dip that arises from surface roughness, accompanied by a detailed demonstration of the physical mechanism at play. The mode coupling characteristics of an AT-cut quartz crystal plate are systematically studied under different temperature profiles, considering surface roughness to follow a Gaussian distribution, using two-dimensional thermal field equations. Analysis of free vibration, achieved via COMSOL Multiphysics's partial differential equation (PDE) module, reveals the resonant frequency, frequency-temperature curves, and mode shapes of the quartz crystal plate. Calculating the admittance and phase response curves for a quartz crystal plate under forced vibration conditions utilizes the piezoelectric module. Studies involving both free and forced vibration analyses indicate that the resonant frequency of a quartz crystal plate is affected negatively by surface roughness. Subsequently, mode coupling is more apt to appear in a crystal plate with surface roughness, causing a dip in performance as the temperature shifts, hence decreasing the stability of quartz crystal sensors, and thus its exclusion in device fabrication is recommended.
Utilizing deep learning networks for semantic segmentation is a key method in extracting objects from very high-resolution remote sensing imagery. Semantic segmentation performance has noticeably improved with Vision Transformer networks, contrasting with traditional convolutional neural networks (CNNs). deep fungal infection Vision Transformer networks and Convolutional Neural Networks employ contrasting architectural approaches. Multi-head self-attention (MHSA), image patches, and linear embedding are a few of the primary hyperparameters. Insufficient investigation exists regarding optimal configurations for object detection in high-resolution imagery, and their effect on network performance. Using vision Transformer networks, this article examines the process of identifying building footprints from very high resolution images.