Search Results (1,115)

This work presents a data-driven framework for early detection of polymer melt instability in industrial injection moulding using Long Short-Term Memory (LSTM) time-series models. The study uses six months of continuous production data comprising approximately 280,000 injection cycles collected from a fully operational thermoplastic injection line. Because melt behaviour evolves gradually and conventional threshold-based monitoring often fails to capture these transitions, the proposed approach models temporal patterns in torque, pressure, temperature, and rheology to identify drift conditions that precede quality degradation. A physically informed labelling strategy enables supervised learning even with sparse defect annotations by defining volatile zones as short time windows preceding operator-identified non-conforming parts, allowing the model to recognise instability windows minutes before defects emerge. The framework is designed for deployment on standard machine signals without requiring additional sensors, supporting proactive process adjustments, improved stability, and reduced scrap in injection moulding environments. These findings demonstrate the potential of temporal deep-learning models to enhance real-time monitoring and contribute to more robust and adaptive manufacturing operations. Full article

In the bioprocessing industry, real-time monitoring of bioreactors is essential to ensuring product quality and process efficiency. Conventional monitoring methods can satisfy some needs but suffer from calibration drift, limited spatial coverage, and incompatibility with harsh or miniaturized environments. Optical fiber sensors, with their high sensitivity, remote monitoring capability, compact size, and multiplexing, have become a promising technology for in situ bioreactor monitoring. This review summarizes recent progress in optical fiber sensors for key bioreactor parameters, with an emphasis on pH and dissolved oxygen (DO), and briefly covers temperature and pressure monitoring. Different sensing mechanisms, materials, and fiber architectures are compared in terms of sensitivity, response time, stability, and integration strategies in laboratory and industrial-scale bioreactors. Finally, current challenges and future trends are discussed, including multi-parameter sensing, long-term reliability, and the integration of optical fiber sensors with process analytical technology and data-driven control for intelligent bioprocessing. Full article

This work presents the development of a low-cost and high-performance multi-sensory gas detection instrument named the AIMNet Sensor, with the integration of a machine learning-based data processing method. The compact and low-power instrument (8.5 × 11.5 cm, 1.4 W) houses the core sensing hardware module, Senseair K96, that integrates both a non-dispersive infrared (NDIR)-based gas sensing unit and a BME280 environmental sensing unit. To address the outdoor operation challenges caused by environmental fluctuation due to the varying temperature, humidity, and pressure, from the software aspect, multiple machine learning-based regression models were trained in this work on 13,125 calibration data points collected under controlled laboratory conditions. Among ten tested algorithms, the Multilayer Perceptron (MLP) and Elastic Net models achieved the highest accuracy, with R-squared coefficient $R^2>0.8$ on both indoor and outdoor scenarios, and with inter-sensor root mean square error (RMSE) within 1.5 ppm across four identical instruments. Moreover, field mobile validation was performed near a wastewater management facility using this solution, confirming a strong correlation with LI-COR reference measurements and a reliable detection of ${\mathrm{CH}}_4$ leaks with concentrations up to 18 ppm at the test site. Overall, this machine learning-integrated NDIR sensing solution (i.e., AIMNet) offers a practical and scalable solution towards a more robust distributed ${\mathrm{CH}}_4$ monitoring network for real-world field-deployable applications. Full article

This study describes the developed special prototype of a wearable measuring device based on a photoplethysmography (PPG) sensor. It contains also a humidity sensor and a thermometer to measure skin moisture and temperature, and a force-sensitive (FSR) element to sense a contact pressure between the measuring probe and the skin surface. All parts of the multi-sensor are shielded, to be applicable in a weak magnetic field environment. After the basic sensor’s functionality verification inside the magnetic resonance imaging tomograph, a set of experiments was performed. Comparative measurements by an oximeter confirm good correspondence with heart rate values determined from PPG (HR_PPG) and FSR (HR_FSR) signals—the mean absolute error lies below 0.5 min⁻¹ for both types. The sensing of PPG signals on wrists was realized for Normal, Dry, and Wet skin. In comparison with normal skin conditions, drying decreases the PPG signal range by 7% and the systolic pulse width by 8%, while moistening increases the signal ripple by 3% and decreases the correlation between HR_PPG and HR_FSR values by 5%. The detailed analysis per hand and gender types yields differences between male and female subjects, while the results for left and right hands differ less. Full article

Monitoring the complex, three-dimensional flow within centrifugal compressor diffusers remains a major challenge due to geometric confinement, high rotational speeds, and strong unsteadiness near surge and stall. This review provides a comprehensive assessment of contemporary instrumentation strategies for diffuser flow characterization, spanning pressure, temperature, velocity, vibration, and acoustic measurements. The article outlines the standards governing compressor instrumentation, compares conventional probes with emerging high-resolution and high-bandwidth sensor technologies, and evaluates the effectiveness of pressure- and temperature-based diagnostics, optical methods, and advanced dynamic sensing in capturing diffuser behavior. Case studies from industrial compressors, research rigs, and high-speed experimental facilities illustrate how sensor layout, bandwidth, and synchronization influence the interpretation of flow stability, performance degradation, and surge onset. Collectively, these examples demonstrate that high-frequency pressure and temperature probes remain indispensable for instability detection, while optical techniques such as PIV, LDV, and PSP/TSP offer unprecedented spatial resolution for understanding flow structures. The findings highlight the growing integration of hybrid sensing architectures, digital acquisition systems, and data-driven analysis in diffuser research. Overall, the review identifies current limitations in measurement fidelity and accessibility while outlining promising paths toward more robust, real-time monitoring solutions for reliable centrifugal compressor operation. Full article

Saltwater intrusion increasingly jeopardizes groundwater in low-lying coastal plains worldwide, where the combined effects of sea-level rise, land subsidence, and hydraulic regulation further exacerbate aquifer vulnerability and threaten the long-term sustainability of freshwater supplies. To move beyond sparse and fragmented piezometric observations, we propose “integrated coastal supersites”: wells equipped with multiparametric sensors and multilevel piezometers that couple high-resolution vertical conductivity–temperature–depth (CTD) profiling with continuous hydro-meteorological time series to monitor the hydrodynamic behavior of coastal aquifers and saltwater intrusion. This study describes the installation of two supersites and presents early insights from the first monitoring period, which, despite a short observation window limited to the summer season (July–September 2025), demonstrate the effectiveness of this approach. Two contrasting supersites were deployed in the coastal plain between the Brenta and Adige Rivers (Italy): Gorzone, characterized by a thick, laterally persistent aquitard, and Buoro, where the aquitard is thinner and discontinuous. Profiles and fixed sensors at both sites reveal a consistent fresh-to-saline transition in the phreatic aquifers and a secondary freshwater lens capping the confined systems. At Gorzone, the confining layer hydraulically isolates the deeper aquifer, preserving low salinity beneath a saline, tidally constrained phreatic zone. Groundwater heads oscillate by about 0.2 m, and rainfall events do not dilute salinity; instead, pressure transients—amplified by drainage regulation and inland-propagating tides—induce short-lived EC increases via upconing. Buoro shows smaller water-level variations, not always linked to rainfall, and, in contrast, exhibits partial vertical connectivity and faster dynamics: phreatic heads respond chiefly to internal drainage and local recharge, with rises rapidly damped by pumping, while salinity remains steady without episodic peaks. The confined aquifer shows buffered, delayed responses to surface forcings. Although the monitoring window is currently limited to 2025 through the summer season, these results offer compelling evidence that coastal supersites are reliable, scalable, and management-critical relevance platforms for groundwater calibration, forecasting, and long-term assessment. Full article

Greenhouse agriculture is experiencing global expansion; however, in Andean countries such as Ecuador, its development is constrained by low-tech infrastructure, limited automation, and insufficient environmental monitoring, all of which negatively affect productivity and fruit quality. This study characterized the microclimate of a low-tech greenhouse in Chaltura, Imbabura Province, during a complete production cycle of tomato crop (Solanum lycopersicum L.). Microclimatic conditions were analyzed during three phenological stages (vegetative, reproductive, and harvest). Temperature and relative humidity were recorded at 5 min intervals using sensors placed in the greenhouse quadrants, while an external weather station provided daily outdoor climate data. Statistical analyses were performed in R software (version 4.4.x). The results revealed marked internal microclimatic heterogeneity and showed that the crop remained outside the optimal ranges of temperature, relative humidity, and vapor pressure deficit (VPD) for over 50% of the time across all phenological stages and greenhouse quadrants. These findings underscore the urgent need for cost-effective climate-control strategies adapted to local conditions and provide a scientific basis for future research aimed at improving climatic and productive efficiency, as well as the resilience and sustainability of protected agriculture in Andean regions. Full article

Background/Objectives: All the industry standard methods for monitoring the freeze-drying process, from the single-vial assessment using temperature probes, such as thermocouples, to batch assessments using comparative pressure measurements, have poorly defined transitions marking the end of ice sublimation. In this study, through-vial impedance spectroscopy (TVIS) is used to characterise and validate the point on the Pirani curve that corresponds to the end of ice sublimation. The impact of the solution composition in relation to its propensity to form crystalline and amorphous domains and the impact of the batch size were investigated. Methods: Individual TVIS vials were placed at specific positions across the shelf, in order to represent the core and edge vials of the batch. The unique features of the high-frequency real part capacitance, with its precise sublimation endpoint-defining plateau, were then used to map the individual-vial sublimation endpoints onto the Pirani profile, with a view to predicting the batch sublimation endpoint. Results: TVIS vial endpoints enabled a key observation that the shape of the Pirani profile may be analysed in terms of two phases, the first being largely associated with ice sublimation and the second being associated with water desorption. Moreover, by identifying the transition point more precisely, even in the small to intermediate scale systems, we provide a scientific basis for predicting the sublimation endpoint for production-scale dryers, where Pirani sensors are already in place. Conclusions: Such qualification of batch sublimation endpoints would allow for earlier, confident switching to the secondary drying stage without unnecessary delay, leading to shorter cycles, reduced energy consumption, and improved utilisation of costly freeze-drying infrastructure. Full article

A multimodal sensor was developed to record dissolved oxygen, L*a*b* colour, temperature, and pH. This work builds on an existing model that correlates blood oxygen saturation with L*a*b* colour values. An L*a*b* colour sensor was constructed from an RGB sensor and validated against a commercial colourimeter. Sensor performance was confirmed using reference colours. Dissolved oxygen was measured with a screen-printed electrode and an analogue-to-digital converter. The results highlight potential for future optical determination of oxygen saturation, combined with electrochemical measurement of oxygen partial pressure, and compensation for pH and temperature. Full article

The development of hydrogen energy is advancing rapidly, while the progress of hydrogen sensors has been relatively lagging behind and cannot meet the stringent performance requirements of hydrogen energy applications. WO₃ has attracted significant attention due to its highly complementary optical and electrical responses to hydrogen. In this study, hydrogen-sensitive WO₃ thin films characterized by vertically aligned crystallites were fabricated by modulating the substrate temperature and oxygen pressure during pulsed laser deposition. Building upon this foundation, a comprehensive investigation into surface modification strategies for enhancing sensitivity was undertaken. The surface modifications encompassed eight distinct metals and four different metal oxides. Among the metal-modified samples, palladium (Pd) Pd exhibited a markedly enhanced electrical response, defined as the ratio of the resistance in hydrogen-free air to that in hydrogen, of 1022, corresponding to ~45 times higher than the value of 22.4 achieved for Pt-modified films and 120 times higher than the value of 8.4 for Au-modified films. In addition, Pd/WO₃ films showed a measurable optical transmittance change of 9.7%, while all other metal-modified samples exhibited negligible optical responses (<1%). This enhancement is attributable to the catalytic and electronic sensitisation effects of Pd. Conversely, metals such as platinum (Pt), gold (Au), and silver (Ag) elicited negligible optical responses, suggesting minimal catalytic activity. The electrical response in these cases was primarily governed by electronic sensitization effects related to the work function of the metal, with higher work function values correlating with more pronounced sensitization. Regarding metal oxide modifications, the sensitization effect was more substantial when the disparity in work function between the oxide and WO₃ was greater, and this enhancement was found to be independent of the charge carrier type of the modifying oxide. These results offer significant insights into the design principles underlying high-performance WO₃-based hydrogen sensors and underscore the pivotal influence of surface modification in governing their sensing characteristics. Full article

This paper addresses the issue of abnormal fluctuations in brake pipe pressure causing variations in braking force, or even forced stops, in heavy-haul trains. A multi-parameter synchronous acquisition monitoring device has been designed to collect relevant operational parameters during train movement. Integrating train traction calculation methods, algorithmic reasoning is conducted to assess the impact of abnormal pipe pressure fluctuations on braking force. Utilising the derived computational approach, the effect of such pressure anomalies on train braking force is calculated. Train braking force is regulated through control of the train pipe pressure reduction. Both train pipe pressure and pressure reduction are managed by the locomotive via the equalising air chamber. Traditional detection methods focus on pressure reduction and air charging/discharging times, making it difficult to analyse fluctuation causes in-depth. This study installs pressure sensors on the locomotive brake’s equalising air chamber and the train pipe inspection port to collect pressure data. It simultaneously records parameters such as ambient temperature and atmospheric pressure. Utilising the monitoring data, it calculates the impact of pipe pressure fluctuations on train air braking force, thereby supporting improvements in braking system stability and operational safety. Full article

This review summarizes recent progress and emerging trends in multiparameter optical fiber sensing, emphasizing techniques that enable the simultaneous measurement of temperature, strain, acoustic waves, pressure, and other environmental quantities within a single sensing network. Such capabilities are increasingly important for structural health monitoring, environmental surveillance, industrial diagnostics, and geophysical observation, where multiple stimuli act on the fiber simultaneously. The paper outlines the physical principles and architectures underlying these systems and focuses on strategies for compensating and decoupling cross-sensitivity among measured parameters. Special attention is devoted to advanced distributed sensing schemes based on coherent optical frequency-domain reflectometry (C-OFDR), coherent phase-sensitive time-domain reflectometry (Φ-OTDR), and Brillouin optical time-domain reflectometry (BOTDR). Their theoretical foundations, their signal-processing algorithms, and the design modifications that improve parameter discrimination and accuracy are analyzed and compared. The review also highlights the roles of polarization and mode diversity and the growing application of machine-learning techniques in the interpretation and calibration of data. Finally, current challenges and promising directions for the next generation of fiber-optic multiparameter sensors are outlined, with a view toward high-resolution, low-cost, and field-deployable solutions for real-world monitoring applications. Full article

Anaerobic reactors are often used to control emissions and capture greenhouse gas (GHG) (biogas, a mixture of carbon dioxide and methane) from waste such as dairy manure. However, real-time monitoring of biogas production during in vitro anaerobic experiments is often challenging mainly due to the unpredictable and low levels of biogas production in a lab reactor system. The application of Internet of Things (IoT) technologies can enhance real-time monitoring of biogas production and GHG emissions from livestock waste. Integration of IoT to anaerobic reactors provides transformative solutions for low-cost monitoring. In this study, an IoT based sensor system that included a highly sensitive Renesas mass flow sensor module for biogas monitoring, Adafruit ported pressure sensor for monitoring of reactor pressure, and ultra-small DROK temperature probe for temperature monitoring was built and implemented for determining the biogas production in anaerobic reactors. Further, impacts of anaerobic process on the reduction of pathogenic organisms such as E. coli were determined using the conventional culture-based method. Results showed that the application of the IoT based system was able to monitor biogas production in real-time, and transmit the data to mobile phone using the ThingSpeak IoT platform offered by MathWorks (MATLAB) (Natick, MA, USA). The difference between the sensor’s biogas volume readings and actual observations over a 30-day time interval was 5–6% indicating the high level of accuracy and low error levels of the system. Further, results showed 1.6–4.8 log reductions of E. coli in effluent of anaerobic reactors indicating substantial impacts of the anaerobic process on pathogen indicator reduction. We anticipate that the system we used in this study has a substantial potential to enhance monitoring of anaerobic reactors and GHG emissions from livestock waste. Full article

An optimized extrusion process is desired for both an environmentally friendly and economically sustainable recycling process. The aim of this study is to simulate the melt conveying zone of a single-screw extruder when using contaminated polymers instead of commonly used pure materials, to optimize a mechanical recycling process, and to reduce the number of measurements needed for rheological input data by using mixing rules. Polypropylene (PP) is blended with a polyamide 12 (PA 12) grade and another PP grade to introduce polymer impurities into the material. The blends are subjected to extrusion experiments in a lab-scale single-screw extruder with pressure and temperature sensors along the barrel. An isothermal analytical simulation model is proposed using representative shear rate values and rheological mixing rules to calculate the pressure distribution along the screw channel throughout the melt conveying zone. The rheological input data for the simulation is taken from high-pressure capillary rheometric measurements, but also substituted with values derived from mixing rules. The results show that the application of the shear viscosity through mixing models yields simulated pressure values similar to those measured in the experiments. With the introduction of representative viscosity into the model, relative deviations of around 5% at certain screw speeds can be achieved. Full article

The safe and reliable operation of energy storage stations critically depends on their thermal management systems, specifically the health states or working conditions of involved sensors, such as temperature, humidity, and pressure sensor. Impacted by several environmental factors, some indiscernible defects including signal drift, elevated noise, and response lag may affect the exact surveillance of batteries, leading to potential combustion or even explosion, which requires fault risk early-warning to support timely maintenance. These multi-sensor environmental factor data typically exhibit mixed characteristics, component coupling, and high uncertainty, thus impacting diagnostic accuracy and robustness. With this motivation, this study proposes a pattern-guided framework for vulnerability diagnosis using Component Importance Measure. A pattern-guided strategy is first designed to perform rule induction and fuzzy processing on discrete and continuous sensor data, respectively, to extract underlying vulnerability-related components. Subsequently, a component Importance Measure, which assesses the impact of individual risks on the whole reliability, is established to achieve unified integration and mapping of previous heterogeneous information, therefore providing multidimensional vulnerability representations. An empirical case study demonstrates the fault detection rate, false alarm control, and diagnostic stability of the proposed framework. Full article