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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.

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.

The design of a MEMS ultrasonic sensor has been presented that exploits the Single Bubble Sonoluminescence (SBSL) phenomenon to realize an energy transduction mechanism from acoustical to electrical domain. In the developed scheme, highly stable laser like short duration light pulses are emitted when ultrasound waves strike a thermally generated microbubble stabilized in a confined volume of 1% xenon-enriched water. The emitted light pulses are detected by an integrated profiled silicon type photodetector to generate ultrastable 100 picoseconds duration current pulses per acoustical cycle. The sensor exhibits energy amplification during the transduction process itself that is not achievable by conventional types of MEMS or non-MEMS acoustical sensors. The cylindrical sensor geometry is 650 μm in diameter and 300 μm in height and is designed to have a sensitivity of 5.56 mA/atm/cycle. The sensor can be used in applications where detection of high pressure ultrasound waves is necessary or as an ultrastable very short duration current pulse generator for use in tissue and nerve repair or microsurgery.

In this work, the potential of tin (IV) 2,3-napthalocyanine dichloride (SnNcCl₂) has been studied for sensing applications due to its hydrophobic nature. The multipurpose sensor was fabricated by depositing 50-nm silver (Ag) electrodes on a glass substrate through vacuum thermal evaporation at pressure of $\sim 10^{-5}$ mbar. With the help of masking, a 40-micron inter-electrode gap between Ag electrodes was developed and then 80-nm film of SnNcCl₂ was thermally deposited in the inter-electrode gap resulting in a surface type Ag/SnNcCl₂/Ag multipurpose sensor and was studied for humidity and temperature sensing. The humidity characterization was carried out at two different frequencies, i.e. 120 and 1kHz in the relative humidity range 35–85% RH and 5.5 and 1.3 times increase was recorded with respect to initial capacitance for both frequencies, respectively. The temperature sensing was studied within a temperature range of 15–80^∘C at 120Hz frequency and 1.3 times increase in capacitance was observed with respect to initial capacitance. The sensor’s important parameters, i.e. response time and recovery time were measured to be 8 and 3s at 120Hz for humidity measurements. The morphology of the SnNcCl₂ thin film was measured by atomic force microscope (AFM) and scanning electron microscope (SEM) showing rough surface favorable for sensing applications. The amorphous structure of the film was confirmed by X-ray diffraction (XRD) while optical bandgap was calculated from ultraviolet-visible (UV-vis) spectroscopy.

SINGAPORE – Singapore eHealth Innovations Summit Announces the First EMRAM Stage 7 Hospital in Singapore and Emphasized Technology as Transformative Agent in Specialty Functions.

TAIWAN – Health2Sync Strategically Partners with Taiwan's Ministry of Health and Welfare in Asia's First Government Supported Online Diabetes Care Program.

UNITED STATES – Scientists Identify Protein Involved in Restoring Effectiveness of Common Treatment for Breast Cancer.

UNITED STATES – Scientists Reveal How Signals from Pathogenic Bacteria Reach Danger Sensors of Cells.

UNITED STATES – Scientists Find New Path in Brain to Ease Depression.

UNITED STATES – Tips for Living a Heart Healthy Lifestyle.

CANADA – Review Suggests Eating Oats Can Lower Cholesterol as Measured by a Variety of Markers.

SOUTH KOREA – CSA Group Opens Highly Advanced Electro - Medical Laboratory in Seoul.

AUSTRALIA – Cynata’s Technology Significant Efficacy in Preclinical Asthma Study.

INDIA – Essilor Launches ‘Love to See Change’ Campaign to Educate People about Need to Preserve Visual Health.

In 2004, Yang and co-workers proposed the extraction of bridge frequencies from the dynamic response of a moving test vehicle [Y. B. Yang, C. W. Lin and J. D. Yau, Extracting bridge frequencies from the dynamic response of a passing vehicle, J. Sound Vib.272 (2004) 471–493] and verified the technique by a field test [C. W. Lin and Y. B. Yang, Use of a passing vehicle to scan the bridge frequencies — An experimental verification, Eng. Struct.27(13) (2005) 1865–1878]. This technique was extended to construction of mode shapes [Y. B. Yang, Y. C. Li and K. C. Chang, Constructing the mode shapes of a bridge from a passing vehicles: A theoretical study, Smart Struct. Syst.13(5) (2014) 797–819] and damage identification of bridges. It was referred to as the indirect method for bridge measurement because no vibration sensors are needed for installation on the bridge, but it only requires one or few vibration sensors on the test vehicle. When compared with the conventional direct method that relies fully on the response of the bridge fitted with vibration sensors, the advantage of the indirect method is clear: mobility, economy, and efficiency. Over the past years, many research studies were conducted along the lines of the indirect method for bridge measurement. Significant advances have been made on various aspects of application. This paper represents a state-of-the-art review of the related research works conducted worldwide. Comments and recommendations will be made at proper places, while concluding remarks including future research directions will be presented at the end of the paper.

The vehicle scanning method (VSM), an indirect approach for bridge measurement, has attracted intensive attention since it was proposed. By this method, a moving test vehicle is employed to detect the “mechanical” properties of the bridge, e.g. frequencies, mode shapes, damages, etc., utilizing the interaction between the two substructures. Compared with the conventional direct approach that requires quite a few sensors and data loggers to be fitted on the bridge, the advantage of the VSM is obvious: mobility, economy, and efficiency. As for railways, the broader vehicle-based techniques have long been used to detect the “geometrical” properties of the track, such as track profiles and rail conditions. Relatively little use has been made of the interaction between the moving vehicle/train and the track/bridge. This paper is a state-of-the-art report of the VSM’s applications to highway bridges and the vehicle-based techniques to railway tracks. It starts with a summary of the pioneering works by Yang and co-workers on the VSM. Then, the applications of the techniques to highway bridges and railway tracks will be separately reviewed. Conclusions will be made, along with future research directions, at the end of the paper.

The vehicle scanning method (VSM), an indirect approach for bridge measurement, has attracted intensive attention since it was proposed by Yang and co-workers in 2004. This method is featured by the fact that no vibration sensors need to be mounted on the bridge, but only one or few vibration sensors are required on the test vehicle. Such an idea has been verified by the field tests, and then quickly extended to construction of mode shapes, identification of damping ratios, and detection of damages for bridges, among others. Compared with the conventional direct method that relies fully on the vibration responses recorded by sensors equipped on the bridge, the advantage of the indirect method is obvious: mobility, economy, and efficiency. Over the years, a rapidly growing number of research works have been conducted along the lines of the VSM for bridge measurement. Particularly, extensive lab experiments and field tests have been carried out worldwide to implement the VSM, resulting in numerous new findings. Moreover, while the technique is still flourishing, it is nourished by inclusion of modern devices such as smartphones, vehicular networks, and cloud. In 2018, a review paper was compiled by two of the authors. To reflect the recent rapid growth of research in this area since then, there exists a need to make an expansion to include the huge number of newly published papers (274 papers in total). As an extension of the 2018 paper, this paper represents a state-of-the-art review of the related researches conducted worldwide. Comments and recommendations will be made at proper places, while concluding remarks including future research directions will be presented at the end of the paper.

The adsorption of cytosine on graphene surface is studied using density functional theory with local density approximation. The cytosine is physisorbed onto graphene through π–π interaction, with a binding energy around -0.39 eV. Due to the weak interaction, the electronic properties of graphene show little change upon adsorption. The cytosine/graphene interaction can be strongly enhanced by introducing metal atoms. The binding energies increase to -0.60 and -2.31 eV in the presence of Li and Co atoms, respectively. The transport behavior of an electric sensor based on Co-doped graphene shows a sensitivity one order of magnitude higher than that of a similar device using pristine graphene. This work reveals that the sensitivity of graphene-based bio-sensors could be drastically improved by introducing appropriate metal atoms.

Thermodynamically stable molybdenum trioxide nanorods have been successfully synthesized by a simple hydrothermal process. The product exhibits high-quality, single-crystalline layered orthorhombic structure (α-MoO₃), and aspect ratio over 20 by characterizations of X-ray diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM) and Fourier transform infrared (FT-IR). The growth mechanism of α-MoO₃ nanorods can be understood by electroneutral and dehydration reaction, which is highly dependent on solution acidity and hydrothermal temperature. The sensing tests show that the sensor based on MoO₃ nanorods exhibits high sensitivity to NO₂ and is not interferred by CO and CH₄, which makes this kind sensor a competitive candidate for NO₂ detection. The intrinsic sensing performance of MoO₃ maybe arise from its nonstoichiometry of MoO₃ owing to the presence of Mo⁵⁺ and oxygen vacancy in MoO₃ lattice, which has been confirmed by X-ray photoelectron spectroscopy (XPS) analysis. The sensing mechanism of MoO₃ for NO₂ is also discussed.

We have successfully synthesized ZnO nanoparticles (NPs) from solution combustion method using combustible fuel (Green gram). XRD pattern confirms that the prepared compound is composed of wurtzite hexagonal zinc-oxide. FTIR spectrum of ZnO NPs shows the band at ~ 417 cm^-1 associated with the characteristic vibration of Zn-O. The UV-Vis spectrum shows a strong absorption band at ~ 365 nm which is blue shifted due to quantum confinement effect. TEM images show the average sizes of the nanoparticles are found to be almost ~ 15–30 nm. The as-synthesized product shows good electrochemical sensing of dopamine. Furthermore the antibacterial properties of ZnO NPs were investigated by their bactericidal activity against four bacterial strains using the agar well diffusion method.

Mesoporous silica monoliths are an attractive area of research owing to their high specific surface area, uniform channels and mesoporous size (2–30nm). This paper deals with the direct templating synthesis of a mesoporous worm-like silica monolithic material using F127 — a triblock copolymer, by micro-emulsion technique using trimethyl benzene (TMB), as the solvent. The synthesized silica monolith is characterized using SEM-EDAX, XRD, BET, NMR and FT-IR. The monolith shows an ordered worm-like mesoporous structure with tuneable through pores, an excellent host for the anchoring of chromo-ionophores for the naked-eye metal ion-sensing. The mesoporous monoliths were loaded with 4-dodecyl-6-(2-pyridylazo)-phenol (DPAP) ligand through direct immobilization, thereby acting as solid-state naked-eye colorimetric ion-sensors for the sensing toxic Pb${}^{\mathrm{\text{(II)}}}$ ions at parts-per-billion (ppb) level in various industrial and environmental systems. The influence of various experimental parameters such as solution pH, limiting ligand loading concentration, amount of monolith material, matrix tolerance level, limit of detection and quantification has been studied and optimized.

In this paper, we report the preparation and characterization of a sensitive and reusable nonenzymatic glucose (NEG) sensor based on copper nanowires (CuNWs)/polyaniline (PANI)/reduced graphene oxide (rGO) nanocomposite ink. The CuNWs/PANI/rGO nanocomposite ink was prepared by solvothermal mixing of CuNWs, PANI, rGO and binders. The X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Fourier Transform Infra-Red (FT-IR) spectroscopy techniques were used to assess the structural and morphological parameters of prepared nanocomposite ink. The cyclic voltammetry (CV) technique was used to estimate the electrochemical behavior of prepared NEG sensor. The structural, morphological and spectroscopy results confirmed the change in morphological and oxidation state of CuNWs to CuO nanostructures as a constituent of nanocomposite ink. The CuO nanostructures supported on PANI/rGO demonstrated good electrochemical stability and great electrocatalytic activity toward glucose oxidation. At a glucose oxidation potential of 0.64V, the prepared NEG sensor exhibited great electrocatalytic ability by offering a high sensitivity of 843.06$\mu$AmM${}^{-1}$cm${}^{-2}$ in the linear glucose range 0–4mM with a lower detection limit of 1.6mM. In addition to these outstanding performance characteristics, CuNWs/PANI/rGO nanocomposite ink-based NEG sensor has the advantages of ease of fabrication, low cost and reusability.

In industrial installations, the piezoelectric sensor plays a very important role in the monitoring of electromechanical systems and the detection of their early defects. Modeling is the mathematical presentation of the operating principle of the piezoelectric sensor, it allows to transform this principle to equations, these equations allow to improve the performances of this sensor and to propose new designs. In this work, the effects of piezoelectric materials are explained and the piezoelectric sensor is described. The physical behavior of the sensor is modeled and extracted a formula relates the accuracy as a function of relative movement (vibratory displacement). The model developed is validated by simulation and by experimental tests and the appropriate choice of the damping rate makes it possible to improve the parameters of the piezoelectric sensor and to progress the vibratory analysis technique.