Search Results (3,502)

Atenolol is a medication belonging to the class of drugs known as beta-blockers, used to treat high blood pressure (hypertension) and irregular heartbeats (arrhythmia). Their presence in the environment has serious impacts on humans, animals, and the water ecosystem. In this context, the aim of this study was to develop a simple voltammetric method for the determination of atenolol (ATN) using carbon paste electrodes modified with the nanomaterials TiO₂ and rGO/TiO₂. The analytical performance of the modified sensor was evaluated using square wave voltammetry and cyclic voltammetry in 0.1 mol L⁻¹ acid sulfuric solution (H₂SO₄), pH 2. The nanocomposite electrode CPE/rGO/TiO₂ exhibited excellent electrocatalytic activity towards ATN oxidations at 0.1 mol L⁻¹ H₂SO₄ compared with unmodified carbon paste electrodes CPEs and those modified with titanium oxide, CPE/TiO₂. Different experimental and conditional parameters were optimized, such as supporting electrolytes, pH, amplitude, frequency, etc. Under optimal conditions, linear calibration curves were obtained, ranging from 1.7 to 23.2 µmol L⁻¹ for ATN with detection limits of 0.05 μmol L⁻¹. The modified nanocomposite CPE/rGO/TiO₂ sensor showed good sensitivity and good repeatability (RSD ≤ 0.61%) for ATN determination. The proposed sensor is mechanically robust and presented reproducible results and a long useful life. In order to verify the usefulness of the developed methods, the nanocomposite sensor CPE/rGO/TiO₂ was applied for the detection of atenolol in real samples (pharmaceutical tablets without any pre-treatment). The excipients present in the tablets did not interfere in the assay. Recoveries ranging from 97.7% to 106% were obtained. The results showed that the CPE/rGO/TiO₂ voltammetric sensor could be successfully applied in the routine quality control of ATN in complex matrices. Full article

This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector. Full article

For most landslides, several destabilising processes act simultaneously, leading to relative sliding along the soil or rock mass surface over time. A number of machine learning approaches have been proposed recently for accurate relative and cumulative landside displacement prediction, but researchers have limited their studies to only a few indicators of displacement. Determining which influencing factors are the most important in predicting different stages of failure is an ongoing challenge due to the many influencing factors and their inter-relationships. In this study, we take a data-driven approach to explore correlations between various influencing factors triggering slope movement to perform dimensionality reduction, then feature selection and extraction to identify which measured factors have the strongest influence in predicting slope movements via a supervised regression approach. Further, through hierarchical clustering of the aforementioned selected features, we identify distinct types of displacement. By selecting only the most effective measurands, this in turn informs the subset of sensors needed for deployment on slopes prone to failure to predict imminent failures. Visualisation of the important features garnered from correlation analysis and feature selection in relation to displacement show that no one feature can be effectively used in isolation to predict and characterise types of displacement. In particular, analysis of 18 different sensors on the active and heavily instrumented Hollin Hill Landslide Observatory in the north west UK, which is several hundred metres wide and extends two hundred metres downslope, indicates that precipitation, atmospheric pressure and soil moisture should be considered jointly to provide accurate landslide prediction. Additionally, we show that the above features from Random Forest-embedded feature selection and Variational Inflation Factor features (Soil heat flux, Net radiation, Wind Speed and Precipitation) are effective in characterising intermittent and explosive displacement. Full article

Deep cavity configurations are common in various industrial applications, including automotive windows, sunroofs, and many other applications in aerospace engineering. Flows over such a geometry can result in aeroacoustic coupling between the cavity shear layer oscillations and the surrounding acoustic modes. This phenomenon can result in a resonance that can lead to significant noise and may cause damage to mechanical structures. Flow control methods are usually used to reduce or eliminate the aeroacoustic resonance. An experimental set up was developed to study the effectiveness of both a cylinder and a profiled cylinder positioned upstream from the cavity in reducing the flow resonance. The cavity flow and the acoustic signals were obtained using particle image velocimetry (PIV) and unsteady pressure sensors, respectively. A decrease of up to 36 dB was obtained in the sound pressure levels (SPL) using the passive control methods. The profiled cylinder showed a similar efficacy in reducing the resonance despite the absence of a high-frequency forcing. Time-space cross-correlation maps along the cavity shear layer showed the suppression of the feedback mechanism for both control methods. A snapshot proper orthogonal decomposition (POD) showed interesting differences between the cylinder and profiled cylinder control methods in terms of kinetic energy content and the vortex dynamics behavior. Furthermore, the interaction of the wake of the control device with the cavity shear layer and its impact on the aeroacoustic coupling was investigated using the POD analysis. Full article

Objectives: Hypersensitive acupoints in specific body areas are associated with corresponding internal or visceral disorders. Back-shu points are clinically significant for the diagnosis of visceral organ disease, according to the biomechanical characteristics of the acupoints. In this study, we assessed the biomechanical characteristics and pain sensitivities of five back-shu points linked to five visceral organs in healthy participants. Methods: The study included 48 volunteer participants. A myotonometry was used to assess muscle tone and muscle stiffness at five back-shu points associated with visceral organs. Pressure was monitored using a microcontroller and a force sensor. Pain sensitivity was assessed in response to deep pressure pain produced by a constant force. Results: Substantial differences in muscle tone and stiffness were observed at the five back-shu points; muscle tone was highest at BL15, whereas muscle tone and muscle stiffness were lowest at BL23. Moreover, pain sensitivity was significantly different among the acupoints; pain sensitivity was highest at BL23. There was a significant negative correlation between muscle tone and pain sensitivity. Conclusions: We found significant differences in muscle tone, muscle stiffness, and pain sensitivity among five back-shu points associated with visceral organs, which may be attributable to anatomical variations at each point. Our findings suggest that differences at back-shu points should be considered to ensure the accurate diagnosis of visceral disease. Full article

The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and Convolutional Neural Network) and three Supervised Machine Learning (SML) methods (Least Squares, Support Vector Regression, and Random Forest (RF)). Data were collected from nine subjects across six activities: normal and slow walking, static with and without carrying a load, and two Manual Material Handling activities. This study has two main contributions: first, the estimation of GRF components (Fx, Fy, and Fz) during the six activities, two of which have never been studied; second, the comparison of the accuracy of GRF component estimation between the six methods for each activity. RF provided the most accurate estimation for static situations, with mean RMSE values of RMSE_Fx = 1.65 N, RMSE_Fy = 1.35 N, and RMSE_Fz = 7.97 N for the mean absolute values measured by the force plate (reference) RMSE_Fx = 14.10 N, RMSE_Fy = 3.83 N, and RMSE_Fz = 397.45 N. In our study, we found that RF, an SML method, surpassed the experimented DL methods. Full article

A circular non-touch mode capacitive pressure sensor can operate in both transverse and normal uniform loading modes, but the elastic behavior of its movable electrode plate is different under the two different loading modes, making its input–output analytical relationships between pressure and capacitance different. This suggests that when such a sensor operates, respectively, in transverse and normal uniform loading modes, the theory of its numerical design and calibration is different, in other words, the theory for the transverse uniform loading mode (available in the literature) cannot be used as the theory for the normal uniform loading mode (not yet available in the literature). In this paper, a circular non-touch mode capacitive pressure sensor operating in normal uniform loading mode is considered. The elastic behavior of the movable electrode plate of the sensor under normal uniform loading is analytically solved with the improved governing equations, and the improved analytical solution obtained can be used to mathematically describe the movable electrode plate with larger elastic deflections, in comparison with the existing two analytical solutions in the literature. This provides a larger technical space for developing the circular non-touch mode capacitive pressure sensors used for measuring the static gas pressure (belonging to normal uniform loading). Full article

F-P (Fabry–Perot) pressure sensors have a wide range of potential applications in high-temperature, high-pressure, and high-dynamic environments. However, existing demodulation methods commonly rely on spectrometers, which limits their application to high-frequency pressure signal acquisition. To solve this problem, this study developed a self-compensated, three-wavelength demodulation system composite with an F-P pressure sensor and a thermocouple to construct a comprehensive sensing system. The system produces accurate pressure measurements in high-temperature, high-pressure, and high-dynamic environments. In static testing at room temperature, the sensing system shows excellent linearity, and the pressure sensitivity is 158.48 nm/MPa. In high-temperature testing, the sensing system maintains high linearity in the range of 100 °C to 700 °C, with a maximum pressure-indication error of about 0.13 MPa (0~5 MPa). In dynamic testing, the sensor exhibits good response characteristics at 1000 Hz and 5000 Hz sinusoidal pressure frequencies, with a signal-to-noise ratio (SNR) greater than 37 dB and 45 dB, respectively. These results indicate that the sensing system proposed in this study has significant competitive advantages in the field of high-temperature, high-speed, and high-precision pressure measurements and provides an important experimental basis and theoretical support for technological progress in related fields. Full article

The article introduces datasets representing piston pump failures along with the experimental evaluation of various machine learning classification models. It starts with a detailed description of three classification datasets consisting of three different levels of valve plate damages and signals recorded from sensors used in classical hydraulic systems (pressure, temperature, flow). The obtained datasets consist of 100k (Failure 1), 30k (Failure 2) and 30k (Failure 3) samples and eight attributes. Then a broad range of classifiers are evaluated including three ensemble models based on decision trees: Random Forest, Gradient-Boosted Trees, and Rotation Forest, as well as the kNN algorithm and a neural network. The analysis showed that neural networks achieved the highest prediction accuracy, enabling a prediction accuracy level of 89%. The kNN algorithm ranked second, and tree-based algorithms performed 4% worse than the neural network. Next, the attribute importance analysis revealed that leak flow, pressure output, pressure of the leak line, and oil temperature are the most important parameters for accurate predictions. Additionally, the research includes a sensitivity analysis of the best classifier to verify the impact of sensor measurements or other noise indicators on the prediction model performance. The analysis indicates a 5% margin of measurement quality. Full article

The accuracy of aerial work platform weighing is essential for safety. However, in practice, the same weight placed at different locations on the platform can yield varying readings, which is a phenomenon known as eccentric load. Measurement errors caused by eccentric loads can lead to missed detections and false alarms in the vehicle safety system, seriously affecting the safety of aerial work. To overcome the influence of eccentric load, the current engineering practice relies on multiple measurements at multiple points and averaging the results to eliminate the eccentric load, which greatly increases the work intensity of workers. To address the aforementioned issues, this paper proposes a three-dimensional force/torque shear force compensation scheme based on bending torque and torsional torque for pressure. The goal is to ensure that the sensor on the aerial work vehicle platform can accurately measure the anti-eccentric load under single-point measurement conditions. A three-box structure anti-eccentric load-weighing sensor for the aerial work platform was designed. Its structure has the advantages of high mechanical strength and no radial effect, ensuring the safety of aerial work, improvement of measurement sensitivity, and enabling of real-time and accurate acquisition of force/torque in three directions. In order to further improve the measurement accuracy of 3D force/torque compensation, a particle swarm optimization algorithm was adopted to optimize the 3D force/torque shear force compensation, thereby improving the safety of engineering operations. Through the verification of a self-made testing platform, the anti-eccentric load sensor designed in this study can ensure that the measurement error of objects at any position on the platform is less than 1.5%, effectively improving the safety of high-altitude platform engineering operations. Full article

Hydrodynamic linear guides in machine tools offer a high load capacity and excellent damping characteristics, improving stability, precision, and vibration reduction. This study builds on previous research where floating heights were verified with a simulation model limited to measured floating heights. Advancements include incorporating pressure sensors into a fixed steel rail, enabling simultaneous measurement of oil film pressure and floating heights for a comprehensive understanding of lubrication conditions within the lubrication gap. The experimental results explore the effects of different lubrication methods, providing valuable insights into cavitation and lubrication adequacy. The results demonstrate the feasibility of utilizing pressure sensors to measure oil film pressure within the lubrication gap, providing a nuanced understanding of lubrication dynamics. By measuring both floating heights and pressure measurement, distinctions between hydrodynamic lubrication, mixed friction regions, and instances of lubricant deficiency become readily discernible. The variations in real-time oil film pressure and floating heights help to optimize the lubrication cycle for hydrodynamic linear guides, enhancing system performance and longevity. Full article

The engine in-cylinder pressure is a very important parameter for the optimization of internal combustion engines. This paper proposes an alternative recursive Kalman filter-based engine cylinder pressure reconstruction approach using sensor-fused engine speed. In the proposed approach, the fused engine speed is first obtained using the centralized sensor fusion technique, which synthesizes the information from the engine vibration sensor and engine flywheel angular speed sensor. Afterwards, with the fused speed, the engine cylinder pressure signal can be reconstructed by inverse filtering of the engine structural vibration signal. The cylinder pressure reconstruction results of the proposed approach are validated by two combustion indicators, which are pressure peak $P_{\max }$ and peak location $P_{\mathrm{loc}}$. Meanwhile, the reconstruction results are compared with the results obtained by the cylinder pressure reconstruction approach using the calculated engine speed. The results of sensor fusion can indicate that the fused speed is smoother when the vibration signal is trusted more. Furthermore, the cylinder pressure reconstruction results can display the relationship between the sensor-fused speed and the cylinder pressure reconstruction accuracy, and with more belief in the vibration signal, the reconstructed results will become better. Full article

This work describes a specialized optical fiber hybrid sensing configuration conceived to monitor internal physical parameters (temperature and pressure) within Li-ion batteries (LiBs) and correlate them with electrochemical performance in operando. The batteries underwent thorough cycling tests under C/3 and C/5 operating rate conditions. Throughout the cycling tests, the optical fiber sensors revealed a compelling correlation between internal and external temperature behavior. Additionally, the application of differential voltage analysis derivative curves during battery operation unveiled insights into the relationship between pressure and temperature changes and the batteries’ electrochemical performance. This optical sensing approach contributes to an understanding of internal LiB dynamics, offering implications for optimizing their performance and safety across diverse applications. Full article

Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly. Full article

With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity. Full article