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The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

With the development of computer technology, its application in athletes’ motion capture is more and more extensive, which can be used to design suitable sensors by the Surendra algorithm. In recent years, more and more scholars have begun to use motion capture technology to study human motion posture, analyze and study human motion posture data and apply it to people’s work, study and life. Motion capture technology has also become a key technology in the field of human motion posture research and is playing an increasingly important role. In this paper, a three-dimensional skeletal model of the athlete’s body is first established based on dynamics, and the athlete’s movement characteristics are simulated by this model. Then, the athlete’s movement posture is judged to determine the appropriate form of movement expression. Then, the improved Surendra algorithm is used to detect the movement movements.

Explosive power is one of the key basic indicators for improving the best athletic performance of athletes. The exploration of explosive power has always been an important topic in the field of physical education and research. Artificial intelligence technology has been widely used in the field of sports. Based on the explosive power data collected by the tension pressure sensor and speed acquisition sensor, multi-sensor data fusion technology is introduced to comprehensively analyze the key factors affecting the explosive power of athletes. By analyzing the theoretical basis of data fusion, the algorithm structure of multi-source data fusion is described from multiple levels and the mathematical model of simple multi-Bayesian data fusion algorithm is optimized and updated. Based on the characteristics of motion sensor data, a scheme to improve the optimization process of the fusion algorithm is proposed to further improve the anti-interference and accuracy of data fusion. Through the simulation performance test, this research has good application value. Through simulation performance testing, we have verified the effectiveness of the system and found that it has a good application value in athlete explosive power analysis. This system can accurately evaluate the explosive power level of athletes, provide scientific training guidance for coaches and help athletes improve their sports performance.

Graphene has amazing applications for sensors due to its excellent performances like high strength and good conductivity, but the transfer issue is in the way of its application perspective. Direct growth of spherical graphene films (SGFs) on cemented carbide may offer a good avenue for various applications in sensor technology, especially for electrochemical sensors. Four common methods for graphene preparation are chemical stripping, chemical vapor deposition (CVD), metal catalysis, and laser fabrication; and subject to transfer issues during usage. In order to overcome this limitation, the fabrication of in-situ growth of SGFs on carbide is proposed as a solution for constructing sensor matrices. This review explores various in-situ SGFs and their potential applications in sensors. The findings presented here shed light on transfer-free graphene with controllable structures that can serve as excellent candidates for sensor matrices.

Thunder God Vine has multiple effects such as anti-inflammatory, anti-tumor, and anti-fertility. One of its main active ingredients, triptriolide, can exert anti-tumor effects by targeting transcription factors, kinases, or inhibiting the ubiquitin/proteasome pathway to inhibit cell proliferation and promote tumor cell apoptosis. However, its high toxicity and poor water solubility limit its application. This study aims to develop a novel photoelectrochemical (PEC) sensor based on MoS₂/Bi₂MoO₆ nanohybrids for the rapid and accurate detection of triptriolide. The PEC sensor is a fundamental tool for extracting and identifying valuable bioactive compounds from this plant. Under optimal conditions, 0.01–10ngmL${}^{-1}$ of triptriolide could be identified, with the detection limit being as low as 0.005ngmL${}^{-1}$. With advantages like rapid response, high sensitivity and preferable stability, the developed sensor is suitable for real-time monitoring of triptriolide levels in actual samples.

We report the results of our experimental measurements of the velocity of sound in response to changes in temperature and air pressure. We used a circuit made with an ultrasonic sensor and a basic microcontroller to make these measurements. The apparatus was kept in a temperature- and air pressure-controlled chamber. Our experimental results showed that as air pressure drops, so does sound velocity. It was discovered that the experimental findings closely matched the theoretical formulations given in the literature. To express sound velocity in ideal and real gases in terms of experimentally controllable variables, we modified the equation found in the literature. We have experimentally shown that air behaves differently between an ideal gas and a real gas below a critical air pressure. It has also been noted that this critical threshold depends on the surrounding temperature. Our experimental findings validate a theoretical formulation that has been largely neglected for over 20 years.

Hematoporphyrin IX, H₂HMP, 8,13-bis(1-hydroxyethyl)-3,7,12,17-tetramethyl-21H,23H-porphine-2,18-dipropionic acid and protoporphyrin IX, H₂PP, 8,13-divinyl-3,7,12,17-tetramethyl-21H,23H-porphine-2,18-dipropionic acid were efficiently immobilized on niobium oxide grafted on a silica gel surface, SiO₂/Nb₂O₅, by the -COO–Nb bond formed between the porphyrin carboxyl groups and the grafted Nb₂O₅. These immobilized porphyrins, SiO₂/Nb₂O₅/H₂HMP and SiO₂/Nb₂O₅/H₂PP, were further reacted with Co(II) in dimethylformamide, resulting in SiO₂/Nb₂O₅/CoHMP and SiO₂/Nb₂O₅/CoPP metallated complexes. The UV-vis spectra of the solid materials showed changes of the Q-bands (a_2u → e_g transition) upon metallation, indicating that by incorporation of Co(II) in the porphyrin ring the local symmetry changed from D_2h to D_4h. These materials, when incorporated in carbon paste electrodes, presented the property of electrocatalyzing O₂ reduction. Rotating disk experiments were performed in order to estimate the number of electrons involved in the process. It was observed that, for both modified electrodes, O₂ was reduced to water in a four-electron process. Amperometric studies showed the potentiality of both modified electrodes as sensors for the determination of dissolved dioxygen. The response time was less than 3 s. A linear response for both systems was obtained between 2 and 12 ppm.

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov–Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

MEMS research has been carried out through industry-university (Tohoku) collaboration for practical applications. Sophisticated devices such as electrostatically levitated rotational gyroscope, MEMS relay for wafer level packaging, array MEMS including multi-probe data storage and multi-column electron beam lithography system, small diameter fiber optic pressure sensor and SiC micro structure or glass press molding, have been developed. Electrical feedthroughs in glass play important role in the wafer level packaging and array MEMS. Materials such as conductive polymer for recording media, carbon nanotube for electron field emitter, SiC for harsh environment are used in these MEMS because of their unique features.

This paper reviews our work on the development of microwave carbon nanotube resonator sensors for gas detection. The sensor consists of a radio frequency resonator coated with a layer of carbon nanotubes. Upon exposure to gasses, the resonant frequency of the sensor shifts to indicate the presence of gasses. Our experimental results demonstrate that the microwave carbon nanotube resonator sensor achieves a sensitivity of 4000 Hz/ppm upon exposure to ammonia and the resonant frequency is recovered when ammonia is evacuated. The sensing mechanism is dependent on electron transfer from the ammonia to the nanotubes. This sensor platform has great potential for wireless sensing network applications.

Visualization is primarily utilized as a training method to enhance athletic movement quality, increase concentration power, and minimize competition stress on the player while building firm confidence. Physical literacy (PL) provides a valuable lens for analyzing physical activity (PA) movement in more significant social and affective learning processes. This paper presents an Interactive Visualization positioning in physical education (IVPPE) to deal with the signal fluctuations and positioning techniques in visualizing Deep Neural Network (DNN). To ensure the success of their game, athletes are always looking for new ways to improve their health and performance. Using sensors to keep tabs on training and recovery has become more popular among athletes. Currently, sports teams are using sensors to track both the players’ internal and external workloads. It illustrates the multilayer localizer (MLL) based on transfer learning to improve the positioning accuracy and physical literacy positioning model (PLPM) as a health determinant. A variety of data augmentation techniques are used to combat signal fluctuations. As a result, the combined effects of motivation-promoting physical activity-based visualization improve the accuracy ratio to 96.7%, prediction ratio to 96.2%, efficiency ratio to 96.8%, and reduce the error rate to 18.7%, stress level (52.8%) compared to other conventional models and have a positive impact on the localizer and positioning, making a difference in physical activity (PA) levels.

We discovered that Sr₂MgSi₂O₇:Eu phosphor emits blue light under the application of a mechanical stress, a phenomenon known as mechanoluminescence (ML). The ML showed a similar spectrum as photoluminescence (PL), which indicated that ML is emitted from the same center of Eu²⁺ ions as PL. The analysis of structure and thermoluminescne suggested that the origin of ML for Sr₂MgSi₂O₇:Eu phosphor can be attributed to strain-induced electroluminescence, that is, piezoelectricity impelled the trapped electrons to escape from the trap and produce ML. Furthermore, the relation between ML intensity and compressive load is close to linearity, which indicate that this sample can be used for smart-skin and self-diagnosis applications.

Cobalt and Nickel Phthalocyanines (CoPc, NiPc) were synthesised by chemical route. Phthalocyanines (Pcs) were characterized by different techniques such as, XRD, UV Visible, and FTIR. Samples in the pellet form were prepared for gas sensing applications. The effect of NO₂ at different concentrations in air at room temperature on the electrical conductivity of CoPc and NiPc has been studied. Sensitivity, response time and recovery time of sensors for different NO₂ concentrations were studied. The comparison of these materials as NO₂ gas sensor is discussed in this research paper.

We review recent works on optomechanics of optically trapped microspheres and nanoparticles in vacuum, which provide an ideal system for studying macroscopic quantum mechanics and ultrasensitive force detection. An optically trapped particle in vacuum has an ultrahigh mechanical quality factor as it is well-isolated from the thermal environment. Its oscillation frequency can be tuned in real time by changing the power of the trapping laser. Furthermore, an optically trapped particle in vacuum may rotate freely, a unique property that does not exist in clamped mechanical oscillators. In this review, we will introduce the current status of optical trapping of dielectric particles in air and vacuum, Brownian motion of an optically trapped particle at room temperature, Feedback cooling and cavity cooling of the Brownian motion. We will also discuss about using optically trapped dielectric particles for studying macroscopic quantum mechanics and ultrasensitive force detection. Applications range from creating macroscopic Schrödinger's cat state, testing objective collapse models of quantum wavefunctions, measuring Casimir force, searching short-range non-Newtonian gravity, to detect gravitational waves.

This paper reports the stress and frequency analysis of dynamic silicon diaphragm during the simulation of micro-electro-mechanical-systems (MEMS) based piezoresistive pressure sensor with the help of finite element method (FEM) within the frame work of COMSOL software. Vibrational modes of rectangular diaphragm of piezoresistive pressure sensor have been determined at different frequencies for different pressure ranges. Optimal frequency range for particular applications for any diaphragm is a very important so that MEMS sensors performance should not degrade during the dynamic environment. Therefore, for the MEMS pressure sensor having applications in dynamic environment, the diaphragm frequency of 280 KHz has been optimized for the diaphragm thickness of 50 $\mu$m and hence this frequency can be considered for showing the better piezoresistive effect and high sensitivity. Moreover, the designed pressure sensor shows the high linearity and enhanced sensitivity of the order of ($\sim$0.5066 mV/psi).

A novel design of a silicon-on-insulator (SOI)-based resonator based on slot micro-ring and Bragg gratings is presented. The corrugated Bragg gratings are structured on both sides of slot micro-ring waveguides. The variation of the effective refractive index is detected by monitoring the shift of the spectral of the resonator. The transmission spectrum and field distribution of the sensor structures are simulated using finite-difference time-domain (FDTD) method. With the combination of the Bragg gratings, the measurement range of the sensor significantly increases without the restriction of a free spectral range (FSR). Our proposed sensor design provides a promising candidate for on-chip integration with other silicon photonic element.

Enhanced sensitivity, precise measurements and accuracy are the key factors to identify the performance of any sensor. In this paper, p-polycrystalline silicon micro-pressure sensor has been designed which works on the principle of piezoresistive effect. A theoretical modeling and computational simulation of the circular Si-diaphragm have been performed through the extensive study of stress and frequency response with the help of finite element method (FEM) within the framework of COMSOL. For a thin diaphragm ($\sim$50 $\mu$m), the Eigen frequency and the frequency generated in a diaphragm under the influence of pressure has been optimized within the pressure range from 1–25 kPa. The modes of vibrations generated in the diaphragm have been optimized at wide-frequency range $\sim$200–800 kHz at various pressure values. The findings of the presented research have suggested that for a $\sim$50 $\mu$m thin diaphragm, the optimized fundamental frequency is $\sim$310 kHz for showing better piezoresistive response which results into enhanced sensitivity. Moreover, the simulation results show that for the designed sensor, the pressure sensitivity of $\sim$11.51 mv/psi has been conveyed.

We propose a novel ultrasonic sensor structure composed of Cantilever arm structure slot dual-micro-ring resonators (DMRR). We present a theoretical analysis of transmission by using the coupled mode theory. The mode field distributions and sound pressure distributions of transmission spectrum are obtained from 3D simulations based on Comsol Multi-physics (COMSOL) method. Our ultrasonic sensor exhibits theoretical sensitivity as high as $1462.5\mathrm{\text{mV}}/\mathrm{\text{kPa}}$, which is 22 times higher than that of the single slot-based micro-ring ultrasonic sensor. Our ultrasonic sensor offers higher sensitivity and a larger detection frequency range than conventional piezoelectric-based ultrasound transducer. The results show that the sensing characteristics of our system can be optimized through changing the position and the angle of sound field. Our ultrasonic sensor is with an area of $25\mu \mathrm{\text{m}}\times 60\mu \mathrm{\text{m}}$, the $Q$-factor can be approximately $1.54\times 10^3$ with radius of $5\mu \mathrm{\text{m}}$. We detect an angular range of $-90^{\circ }$ to $90^{\circ }$ and a minimum distance of $0.01\mu \mathrm{\text{m}}$. Finally, we calculate the Cantilever arm structure slot DMRR array ultrasonic sensor’s optical performance. Our proposed design provides a promising candidate for a hydrophone.

In this paper, the spectral response of uniform and apodized (Gaussian, hyperbolic tangent, apod1, sine, and raised sine) FBGs is analyzed for sensing applications. The reflectivity at Bragg wavelength as well as for sidelobes was assessed as a function of grating length and apodization profiles. The FBG strain and temperature sensors were simulated and a linear response between applied strain or temperature and the wavelength shift is observed. The results indicate that the sensitivity of the sensor is found to be affected both by the grating length and apodization type. The typical strain and thermal sensitivity values are 1.223 pm/$\mu 𝜀$ and 13.60 pm/${}^{\circ }$C, respectively. The results suggest that Gaussian, sine, and raised sine profiles have lower sidelobe strength and reliable sensitivities. The key finding from this study specifies that the ideal grating length must be preferably between 5 and 10 mm for a good sensing behavior.

The measurement of patients’ dosages of radiation caused by medical diagnostics continues to be challenging. A Cantor sequence photonic crystal structure using porous silicon doped with a polymer of polyvinyl alcohol, carbol fuchsin and crystal violet (DPV) is proposed. The influence rules of geometrical and optical parameters such as the radiation doses, number of periods, porosity of porous layers, incident angle and thickness of layers are investigated using MATLAB based on the transfer matrix method. The transmittance of the Cantor sequence of a defective photonic crystal sensor under different conditions is investigated to select the optimum conditions. The proposed system recorded the accepted sensitivity of 0.265nm/Gy, FoM of 6.5Gy${}^{-1}$, Q of 12,701, RS of $6\times 10^{-3}$ and LoD of $8\times 10^{-3}$ for gamma radiation. The suggested detector has simple design, accurate monitoring efficiency and immense potential for gamma radiation sensing.