Search Results (503)

Air pollution threatens human and environmental health worldwide. A Harvard study in partnership with UK institutions found that fossil fuel pollution killed over 8 million people in 2018, accounting for 1 in 5 fatalities worldwide. Iraq, including the Kurdistan Region of Iraq, has a major environmental issue in that it ranks second worst in 2022 due to the high level of particulate matter, specifically PM_2.5. In this paper, a LoRa-based infrastructure for environmental monitoring in the Kurdistan Region has been designed and developed. The infrastructure encompasses end-node devices, an open-source network server, and an IoT platform. Two AirQNodes were prototyped and deployed to measure particulate matter values, temperature, humidity, and atmospheric pressure using manufacturer-calibrated PM sensors and combined temperature, humidity, and atmospheric sensors. An open-source network server is adopted to manage the AirQNodes and the entire network. In addition, an IoT platform has also been designed and implemented to visualize and analyze the collected data. The platform processes and stores the data, making it accessible for the public and decision-making parties. The infrastructure was tested and results validated by deploying two AirQNodes at separate locations adjacent to existing air quality monitoring stations as reference points. The findings demonstrated that the AirQNodes reliably mirrored the trends and patterns observed in the reference monitors. This research establishes a comprehensive infrastructure for monitoring air quality in the Kurdistan Region of Iraq. Furthermore, complete ownership of the system can be attained by possessing and overseeing the critical components of the infrastructure, which encompass the end devices, network server, and IoT platform. This integrated strategy is especially crucial for the Kurdistan Region of Iraq, where cost-efficiency and enduring sustainability are vital, yet such a structure is absent. Full article

Arduino-based sensor systems are gaining widespread adoption in modern technological applications due to their accessibility, low-cost components, diverse sensor compatibility, high reliability, and user-friendly programming. Because of these advantages, such a system was selected to monitor and control microclimate parameters in a small-scale experimental greenhouse. The greenhouse will cultivate several vegetable species in soils with varying zeolite concentrations. The aim of this paper is to present the design and prototype development of a sensor system capable of tracking key environmental parameters, including temperature, humidity, atmospheric pressure, and soil moisture, while also enabling automated irrigation. Full article

This study focuses on a comprehensive analysis of the common methods for road infrastructure monitoring, as well as the perspective of various fiber-optic sensor (FOS) realization solutions in road monitoring applications. Fiber-optic sensors are a topical technology that ensures multiple advantages such as passive nature, immunity to electromagnetic interference, multiplexing capabilities, high sensitivity, and spatial resolution, as well as remote operation and multiple physical parameter monitoring, hence offering embedment potential within the road pavement structure for needed smart road solutions. The main key factors that affect FOS-based road monitoring scenarios and configurations are analyzed within this review. One such factor is technology used for optical sensing—fiber Bragg grating (FBG), Brillouin, Rayleigh, or Raman-based sensing. A descriptive comparison is made comparing typical sensitivity, spatial resolution, measurement distance, and applications. Technological approaches for monitoring physical parameters, such as strain, temperature, vibration, humidity, and pressure, as a means of assessing road infrastructure integrity and smart application integration, are also evaluated. Another critical aspect concerns spatial positioning, focusing on the point, quasi-distributed, and distributed methodologies. Lastly, the main topical FOS-based application areas are discussed, analyzed, and evaluated. Full article

Concrete quality on construction sites depends on maintaining certain workability during transportation. However, traditional slump testing is manual and cannot assess concrete in transit. This study presents a cyber-physical system (CPS) integrating IoT sensors with machine learning models to estimate concrete workability in real time during delivery. The proposed approach first correlates traditional slump test parameters with sensor measurements, allowing for automated replication of established workability evaluation. The CPS continuously captures IoT sensor data, such as drum rotation, vibration, internal pressure, and temperature, processes these data in real time, and infers workability before unloading. The developed CPS was validated in a real-world case study, achieving high workability prediction accuracy with an average error of approximately 0.9 cm and timely, automated feedback of below 200 ms. These results enable continuous in-transit monitoring of concrete workability and lay a practical foundation for data-driven operational improvements in construction logistics. Full article

In loop-powered 4–20 mA transmitter systems, sensors like temperature, pressure, flow, and gas sensors are chosen based on specific application requirements. These systems are widely adopted in high-precision measurement scenarios, including industrial automation, process control, and environmental monitoring. The transmitter requires a high-performance analog front end (AFE) for precise amplification and signal conditioning. This paper presents a low-noise instrumentation amplifier (IA) for high-precision transmitter front ends, featuring a Successive Approximation Register (SAR)-assisted offset calibration architecture. The proposed structure integrates a chopper current-feedback instrumentation amplifier (CFIA) with an automatic offset calibration loop (AOCL), significantly suppressing internal offset errors and enabling high-accuracy signal acquisition under stringent power and environmental temperature constraints. The designed amplifier provides four selectable gain settings, covering a range from ×32 to ×256. Fabricated in a 0.18 μm CMOS process, the CFIA operates at a 1.8 V supply voltage, consumes a static current of 182 μA, and achieves an input-referred noise as low as 20.28 nV/√Hz at 1 kHz, with a common-mode rejection ratio (CMRR) up to 122 dB and a power-supply rejection ratio (PSRR) up to 117 dB. Experimental results demonstrate that the proposed amplifier exhibits excellent performance in terms of input-referred noise, offset voltage, PSRR, and CMRR, making it well-suited for front-end detection in field instruments that require direct interfacing with measured media. Full article

The increasing application of plain bearings in various industries, especially under challenging conditions like thin lubricating films and high temperatures, necessitates effective monitoring to prevent failures and ensure reliable performance. While sensor-based monitoring incurs significant costs and complex installation due to physical sensors and data acquisition systems, model-based tracking offers a more cost-effective alternative. Model-based monitoring relies on mathematical or physics-based models to estimate system behaviour, reducing the need for extensive sensor data. However, reliable results depend on real-time capable and precise simulation models. Conventional real-time modelling techniques, including analytical calculations, empirical formulas, and data-driven methods, exhibit significant limitations in real-world applications. Analytical methods often have a restricted range of applicability and do not match the accuracy of numerical methods. Meanwhile, data-driven approaches rely heavily on the quality and quantity of training data and are inherently constrained to their training domain. Recently, Physics-Informed Neural Networks (PINNs) have emerged as a promising solution for model-based monitoring to capture complex system behaviour. This approach combines physical modelling with data-driven learning, allowing for better generalisation beyond the training domain while reducing reliance on extensive data. Thus, this study presents an approach for load monitoring in radial plain bearings using PINNs. It extends the application of PINNs by relying solely on simple sensor inputs, such as radial load and rotational speed, to predict the hydrodynamic pressure and oil film thickness distribution under varying stationary conditions. The real-time model is trained, validated, and evaluated within and beyond the training domain using elastohydrodynamic simulation results. The developed real-time model enables load monitoring in plain bearings by identifying critical hydrodynamic pressure and oil film thickness values using readily available speed and load sensor data under varying stationary conditions. Full article

Although vacuum insulated panels (VIPs) are known for their exceptional thermal insulation capabilities, their service life is limited due to an increase in internal gas pressure and material aging. In this study, an innovative monitoring system incorporating embedded sensors was developed to estimate the lifespan of VIPs in real time. A test panel was specifically selected to degrade its thermal conductivity over a shortened timeframe to facilitate validation and optimize the experimental duration. Hourly pressure and temperature data collected from the sensors embedded within the panel were analyzed using established pressure–thermal conductivity (λ) relationships from the literature. Based on the time-dependent λ values, a machine learning model employing a random forest regressor was trained to predict the panel’s lifetime. The model demonstrated high accuracy with R² = 0.9999 and RMSE = 0.0017 mW/mK. During the test period, the panel maintained acceptable performance, and the model projected that the critical thermal conductivity threshold of 8.0 mW/mK would be reached at day 66.9. This approach enables continuous, in situ field monitoring of VIP service life without the need for laboratory infrastructure and offers a scalable and practical solution for assessing long-term energy efficiency. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

Defensive pessimism, an important emotion regulation and motivation strategy, has increasingly attracted scholarly attention in psychology. Recently, sensor-based methods have begun to supplement or replace traditional questionnaire surveys in personality research. However, current approaches for collecting vital signs data face several challenges, including limited monitoring durations, significant data deviations, and susceptibility to external interference. This paper proposes a novel approach using a NiCr/NiSi alloy film temperature sensor, which has a K-type structure and flexible piezoelectric pressure sensor to identify and predict defensive pessimism personality traits. Experimental results indicate that the Seebeck coefficients for K-, T-, and E-type thermocouples are approximately 41 μV/°C, 39 μV/°C, and 57 μV/°C, respectively, which align closely with national standards and exhibit good consistency across multiple experimental groups. Moreover, radial artery frequency experiments demonstrate a strong linear relationship between pulse rate and the intensity of external stimuli, where stronger stimuli correspond to faster pulse rates. Simulation experiments further reveal a high correlation between radial artery pulse frequency and skin temperature, and a regression model based on the physiological sensor data shows a good fit (p < 0.05). These findings verify the feasibility of using temperature and flexible piezoelectric pressure sensors to identify and predict defensive pessimism personality characteristics. Full article

The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research contains records with measurements of the air pollutants ozone (O₃) and carbon monoxide (CO), as well as meteorological parameters such as temperature (T), relative humidity (RH), and barometric pressure (BP). This dataset was collected using a set of low-cost sensors over a four-month study period (March to June) in 2024. The monitoring of air pollutants and meteorological parameters was conducted in a city with high industrial activity, heavy traffic, and close proximity to a petrochemical refinery plant. The data were subjected to a series of statistical analyses for visualization using plots that allow for the identification of their behavior. Finally, the dataset can be utilized for air quality studies, public health research, and the development of prediction models based on mathematical approaches or artificial intelligence algorithms. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

Current temperature sensors require regular recalibration to maintain reliable temperature measurement. Photonic/quantum-based approaches have the potential to radically change the practice of thermometry through provision of in situ traceability, potentially through practical primary thermometry, without the need for sensor recalibration. This article gives an overview of the European Partnership in Metrology (EPM) project: Photonic and quantum sensors for practical integrated primary thermometry (PhoQuS-T), which aims to develop sensors based on photonic ring resonators and optomechanical resonators for robust, small-scale, integrated, and wide-range temperature measurement. The different phases of the project will be presented. The development of the integrated optical practical primary thermometer operating from 4 K to 500 K will be reached by a combination of different sensing techniques: with the optomechanical sensor, quantum thermometry below 10 K will provide a quantum reference for the optical noise thermometry (operating in the range 4 K to 300 K), whilst using the high-resolution photonic (ring resonator) sensor the temperature range to be extended from 80 K to 500 K. The important issues of robust fibre-to-chip coupling will be addressed, and application case studies of the developed sensors in ion-trap monitoring and quantum-based pressure standards will be discussed. Full article

Various data such as power grid sensors and manual observed icing, CMA (China Meteorological Administration) Land Surface Data Assimilation System (CLDAS) products, and the Fifth Generation Atmospheric Reanalysis of the Global Climate from Europe Center of Middle Range Weather Forecast (ERA5) are adopted to diagnose an icing process under a cold surge during 16–23 December 2023 in northeastern Yunnan Province. The results show that: (1) in the early stage of the process, mainly the freezing types, such as GG (temperature > 0 °C, relative humidity ≥ 75%) and DG (temperature < 0 °C, relative humidity ≥ 75%), occur. At the end of the process, an increase in icing type as GD (temperature > 0 °C, relative humidity < 75%) appears. (2) Significant differences exist in the elements during different stages of icing, and the atmospheric thermal, dynamic, and water vapor conditions are conducive to the occurrence of freezing rain during ice accretion. The main impact weather systems of this process include a strong high ridge in the mid to high latitudes of East Asia, transverse troughs in front of the high ridge south to Lake Baikal, low altitude troughs, and ground fronts. The transverse trough in front of the high ridge can cause cold air to accumulate and then move eastward and southward. The southerly flows, surface fronts, and other low-pressure systems can provide powerful thermodynamic and moisture conditions for ice accumulation. Full article

There is a lack of an effective approach to measure vacuum conditions inside sealed vacuum electronic devices (VEDs) and other small-space vacuum instruments. In this study, the application performance of an innovative low-pressure gas sensor based on the emission enhancements of multi-walled carbon nanotube (MWCNT) field emitters was investigated, and the in situ vacuum performance of X-ray tubes was studied for the advantages of miniature dimension and having low power consumption, extremely low outgassing, and low thermal disturbance compared to conventional ionization gauges. The MWCNT emitters with high crystallinity presented good pressure sensing performance for nitrogen, hydrogen, and an air mixture in the range of 10⁻⁷ to 10⁻³ Pa. The miniature MWCNT sensor is able to work and remain stable with high-temperature baking, important for VED applications. The sensor monitored the in situ pressures of the sealed X-ray tubes successfully with high-power operations and a long-term storage of over two years. The investigation showed that the vacuum of the sealed X-ray tube is typical at a low 10⁻⁴ Pa level, and pre-sealing degassing treatments are able to make the X-ray tube work under high vacuum levels with less outgassing and keep a stable high vacuum for a long period of time. Full article

Structural Health Monitoring (SHM) is an essential technique for continuously assessing structural conditions using integrated sensor systems during operation. SHM technologies have evolved to address the increasing demand for efficient maintenance strategies in advanced engineering fields, such as civil infrastructure, aerospace, and transportation. However, developing a miniaturized, cost-effective, and multi-sensor solution based on Wireless Sensor Networks (WSNs) remains a significant challenge, particularly for SHM applications in weight-sensitive aerospace structures. To address this, the present study introduces a novel WSN-based Multi-Sensor System (MSS) that integrates multiple sensing capabilities onto a 3 × 3 cm flexible Printed Circuit Board (PCB). The proposed system combines a Piezoelectric Transducer (PZT) for impact detection; a strain gauge for mechanical deformation monitoring; an accelerometer for capturing dynamic responses; and an environmental sensor measuring temperature, pressure, and humidity. This high level of functional integration, combined with real-time Data Acquisition (DAQ) and precise time synchronization via Bluetooth Low Energy (LE), distinguishes the proposed MSS from conventional SHM systems, which are typically constrained by bulky hardware, single sensing modalities, or dependence on wired communication. Experimental evaluations on composite panels and aluminum specimens demonstrate reliable high-fidelity recording of PZT signals, strain variations, and acceleration responses, matching the performance of commercial instruments. The proposed system offers a low-power, lightweight, and scalable platform, demonstrating strong potential for on-board SHM in aircraft applications. Full article