Search Results (5,312)

Soft sensors have emerged as indispensable tools for predicting dissolved gas concentrations in transformer oil-critical indicators for fault diagnosis that defy direct measurement. Addressing the persistent challenge of prediction inaccuracy in existing methods, this study introduces a novel hybrid architecture integrating time-series decomposition, deep learning prediction, and signal reconstruction. Our approach initiates with variational mode decomposition (VMD) to disassemble original gas concentration sequences into stationary intrinsic mode functions (IMFs). Crucially, VMD’s pivotal parameters (modal quantity and quadratic penalty term) governing bandwidth allocation and mode orthogonality are optimized via a Newton–Raphson-based optimization (NRBO) algorithm, minimizing envelope entropy to ensure sparsity preservation through information-theoretic energy concentration metrics. Subsequently, a bidirectional long short-term memory network with attention mechanism (AM-BiLSTM) independently forecasts each IMF. Final concentration trends are reconstructed through superposition and inverse normalization. The experimental results demonstrate the superior performance of the proposed model, achieving a root mean square error (RMSE) of 0.51 µL/L and a mean absolute percentage error (MAPE) of 1.27% in predicting hydrogen (H₂) concentration. Rigorous testing across multiple dissolved gases confirms exceptional robustness, establishing this NRBO-VMD-AM-BiLSTM framework as a transformative solution for transformer fault diagnosis. Full article

Polycyclic aromatic hydrocarbons (PAHs) are increasingly recognized as a major contributor to the occupational cancer risk among firefighters. In response, the National Fire Protection Association (NFPA) and other regulatory bodies have recommended rigorous decontamination protocols to minimize PAH exposure. Despite these efforts, a critical gap persists: the absence of real-time, field-deployable devices capable of detecting these invisible and toxic compounds during firefighting operations or within fire stations. Additionally, the lack of effective and optimized methods for the removal of these hazardous substances from the immediate environments of firefighters continues to pose a serious occupational health challenge. Although numerous studies have investigated PAH detection in environmental contexts, current technologies are still largely confined to laboratory settings and are unsuitable for field use. This review critically examines recent advances in PAH decontamination strategies for firefighting and explores alternative sensing solutions. We evaluate both conventional analytical methods, such as gas chromatography, high-performance liquid chromatography, and mass spectrometry, and emerging portable PAH detection technologies. By highlighting the limitations of existing systems and presenting novel sensing approaches, this paper aims to catalyze innovation in sensor development. Our ultimate goal is to inspire the creation of robust, field-deployable tools that enhance decontamination practices and significantly improve the health and safety of firefighters by reducing their long-term risks of cancer. Full article

Volatile organic compounds (VOCs) present in human exhaled breath have emerged as promising biomarkers for non-invasive disease diagnosis. However, traditional VOC detection technology that relies on large instruments is not widely used due to high costs and cumbersome testing processes. Machine learning-assisted gas sensor arrays offer a compelling alternative by enabling the accurate identification of complex VOC mixtures through collaborative multi-sensor detection and advanced algorithmic analysis. This work systematically reviews the advanced applications of machine learning-assisted gas sensor arrays in medical diagnosis. The types and principles of sensors commonly employed for disease diagnosis are summarized, such as electrochemical, optical, and semiconductor sensors. Machine learning methods that can be used to improve the recognition ability of sensor arrays are systematically listed, including support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and principal component analysis (PCA). In addition, the research progress of sensor arrays combined with specific algorithms in the diagnosis of respiratory, metabolism and nutrition, hepatobiliary, gastrointestinal, and nervous system diseases is also discussed. Finally, we highlight current challenges associated with machine learning-assisted gas sensors and propose feasible directions for future improvement. Full article

Polymer inclusion membranes (PIMs) have undergone substantial advancements in their selectivity and efficiency, driven by their increasing deployment in separation processes, environmental remediation, and sensing applications. This review presents recent progress in the development of PIMs, focusing on strategies to enhance ion and molecule selectivity through the incorporation of novel carriers, including ionic liquids and task-specific extractants, as well as through polymer functionalization techniques. Improvements in mechanical and chemical stability, achieved via the utilization of high-performance polymers such as polyvinylidene fluoride (PVDF) and polyether ether ketone (PEEK), as well as cross-linking approaches, are critically analyzed. The expanded application of PIMs in the removal of heavy metals, organic micropollutants, and gas separation, particularly for carbon dioxide capture, is discussed with an emphasis on efficiency and operational robustness. The integration of PIMs with electrochemical and optical transduction platforms for sensor development is also reviewed, highlighting enhancements in sensitivity, selectivity, and response time. Furthermore, emerging trends towards the fabrication of sustainable PIMs using biodegradable polymers and green solvents are evaluated. Advances in scalable manufacturing techniques, including phase inversion and electrospinning, are addressed, outlining pathways for the industrial translation of PIM technologies. The review concludes by identifying current limitations and proposing future research directions necessary to fully exploit the potential of PIMs in industrial and environmental sectors. Full article

Heyang Fragrance, a traditional incense dating back to the Eastern Han Dynasty (25–220 AD), was recently inscribed on China’s national list of intangible cultural heritage. This study aimed to systematically analyze three variants of Heyang Fragrance (Aicao, Qinqiang, and Jianjia) through integrated methodologies including electronic nose analysis, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), and antimicrobial activity assays. We selected Escherichia coli, Bacillus subtilis, and Candida glabrata for the antimicrobial activity assays. Comparative analysis revealed significant compositional differences between pre- and post-combustion volatile profiles. Upon ignition, sensor response values increased by 50–100% relative to baseline measurements, with sulfides, terpenes, and short-chain alkanes emerging as dominant components. Qinqiang demonstrated the highest odor activity values (OAVs), particularly through carvacrol (OAV = 6676.60) and eugenol (OAV = 2720.84), which collectively contributed to its complex aromatic characteristics. Antimicrobial assessments revealed concentration-dependent efficacy, with Qinqiang exhibiting broad antimicrobial activity against Escherichia coli (11.33 mm inhibition zone) and Bacillus subtilis (15.00 mm), while Jianjia showed maximal effectiveness against Bacillus subtilis (17.67 mm). These findings underscore the dual significance of Heyang Fragrance in cultural conservation and its prospective applications in aroma therapeutic and antimicrobial contexts. Full article

Many buildings in Southern European countries are equipped with both gas boilers and air source heat pumps. The present work concerns an experimental evaluation of indoor comfort in an apartment within a residential building, comparing a gas boiler with cast-iron radiators to an air-to-air heat pump. The comfort conditions inside the apartment are assessed at set-point temperatures of 20 °C and 21 °C and with different water supply temperatures from the gas boiler. Energy consumption data for both heating systems are recorded during the tests. The measurements inside the apartment are conducted using inexpensive, widely accessible sensors and Arduino-like microcontrollers, calibrated before use. As a result, comfort indices for the heat pump are between those for the gas boiler at 20 °C and 21 °C. Additionally, to understand the impact of occupancy, an analysis of local discomfort and air quality was conducted by measuring CO₂ levels, which rose significantly without air exchange. Lastly, the experimental results are compared with previous dynamic and Computational Fluid Dynamics (CFD) analyses, showing the limit of the computational approach. Indeed, the comfort indices derived from the experimental study are superior to those obtained from dynamic simulations and CFD. Full article

The exceptional sensing properties of hydrogen-saturated zigzag phosphorene nanoribbons (ZPNRs-H) for sulfur-containing gases, namely SO₃, SO₂, and H₂S, were investigated using first-principles calculations based on density functional theory. The total energy, adsorption energy, and Mulliken charge transfer were assessed to evaluate the adsorption properties of the ZPNRs-H towards these gases. Notably, the ZPNRs-H exhibits physical adsorption for SO₂ and H₂S gas molecules, while demonstrating chemical adsorption for SO₃, characterized by a substantial adsorption energy and pronounced charge transfer. Furthermore, the adsorption of SO₃ significantly modulates the electronic density of states near the Fermi level of ZPNRs-H. The current–voltage (I–V) characteristics unveil a remarkable enhancement in conductivity post-SO₃ adsorption, underscoring the high sensitivity of ZPNRs-H towards SO₃. Our findings provide profound theoretical insights, heralding the potential of ZPNRs-H as a cutting-edge sensor for SO₃ detection. Full article

The monitoring and prediction of air quality (AQ) is key to minimizing the negative impact of air pollution, as it enables the implementation of corrective measures. An IoT-based multi-purpose monitoring system has therefore been designed. To develop a reliable remote system, this study addresses three challenges: (1) design of a low-cost compact, robust, multi-sensor system, (2) model validation over several months to ensure accurate detection, and (3) the application of machine learning (ML) techniques to classify and predict AQ. The developed system demonstrates a significant cost reduction for regular monitoring, including effective data management under harsh environmental conditions. The prototype integrates pollutant sensors, as well as the detection of liquified petroleum gas, humidity, and temperature. A dataset with more than 30,000 entries per month (data recorded approximately every minute) was saved on the platform. Results identified the three highest pollution categories, highlighting the urgency of addressing AQ in densely populated regions. The ML algorithms allowed us to predict AQ trends with 99.97% accuracy. To summarize, by reducing monitoring costs and enabling large-scale data management, this system offers an effective solution for real-time environmental monitoring. It also highlights the potential of artificial intelligence-based AQ predictions in supporting public health initiatives. This is particularly interesting for developing countries, where pollution control is limited. Future research will develop the models to include data from different environments and seasons, exploring its integration into mobile apps and cloud platforms for real-time monitoring. Full article

Mercury (Hg) is a globally recognized toxic element, and the Minamata Convention on Mercury entered into force in 2017 to address its associated risks. Under the United Nations Environment Programme, international efforts to reduce Hg emissions and monitor its environmental presence are ongoing. In support of these initiatives, we developed a simple and rapid mercury detection device based on a quartz crystal microbalance (QCM-Hg sensor), which utilizes the direct amalgamation reaction between Hg and a gold (Au) electrode. The experimental results demonstrated a proportional relationship between Hg concentration and the resulting oscillation frequency shift. Increased flow rates and prolonged measurement durations enhanced detection sensitivity. The system achieved a detection limit of approximately 1 µg/m³, comparable to that of commercially available analyzers. Furthermore, a measurement configuration integrating the reduction-vaporization method with the QCM-Hg sensor enabled the detection of mercury in aqueous samples. Based on the experimental results and the gas-phase detection sensitivity achieved to date, concentrations as low as approximately 0.05 µg/L appear to be detectable. These findings highlight the potential of the QCM-Hg system for on-site mercury monitoring. This review aims to provide a comprehensive yet concise overview of QCM-Hg sensor development and its potential as a next-generation tool for environmental and occupational mercury monitoring. 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

Ship repair is hazardous, often presenting unsuitable working areas and risks due to the ship’s configuration. Welding tasks are particularly dangerous due to the high temperatures generated, high enough to melt the metal in structural elements, bulkheads, linings, and tanks. This study investigates the consequences of temperature distribution during the welding of naval plates and proposes some accident prevention measures. Industry working conditions were reproduced, including the materials, procedures, and tools used, as well as the certified personnel employed. DH 36-grade naval steel, with a composition of C max. 0.18%, Mn 0.90–1.60%, P 0.035%, S 0.04%, Si 0.10–0.50%, Ni max 0.4%, Cr max 0.25%, Mo 0.08%, Cu max 0.35%, Cb (Nb) 0.05%, and V 0.1%, was welded via FCAW-G (Gas-Shielded Flux-Cored Arc Welding), selected for this study because it is one of the most widely practiced in the naval industry. The main sensor used in the experiments was an FLIR model E50 thermographic camera, and thermal waxes were employed. The results for each thickness case are presented in both graphical and tabular form to provide accurate and actionable guidelines, prioritizing safety. After studying the butt jointing of naval plates of various thicknesses (8, 10, and 15 mm), safe distances to maintain were proposed to avoid risks in the most unfavorable cases: 350 mm from the welding seam to avoid burn injuries to unprotected areas of the body and 250 mm from the welding seam to avoid producing flammable gases. These numbers are less accurate but easier to remember, which prevents errors in the face of hazards throughout a long working day. Full article

Wireless sensor networks (WSNs) have gained significant attention across various industries and scientific fields. Localization, a crucial aspect of WSNs, involves accurately determining node positions to track events and execute actions. Despite the development of numerous localization algorithms, real-world environments pose challenges such as anisotropy, noise, and faults. To improve accuracy amidst these complexities, researchers are increasingly adopting advanced methodologies, including soft computing, software-defined networking, maximum likelihood estimation, and optimization techniques. Our comprehensive review from 2020 to 2024 reveals that approximately 29% of localization solutions employ optimization techniques, 48% of which utilize nature-inspired swarm-based algorithms. These algorithms have proven effective for node localization in a variety of applications, including smart cities, seismic exploration, oil and gas reservoir monitoring, assisted living environments, forest monitoring, and battlefield surveillance. This underscores the importance of swarm intelligence algorithms in sensor node localization, prompting a detailed investigation in our study. Additionally, we provide a comparative analysis to elucidate the applicability of these algorithms to various localization challenges. This examination not only helps researchers understand current localization issues within WSNs but also paves the way for enhanced localization precision in the future. Full article

The development of compact, CMOS-compatible gas sensors is critical for advancing real-time environmental monitoring and industrial diagnostics. In this study, we present a detailed numerical investigation of integrated photonic waveguide designs—such as ridge and slot—optimized for overtone-based gas spectroscopy in the near-infrared range. By evaluating both the evanescent-field confinement and curvature-induced losses across multiple silicon-on-insulator platforms, we identify optimal geometries that maximize light–analyte interactions while minimizing bending attenuation. Our findings provide essential design guidelines for high-performance, low-footprint gas sensors. Full article

Electrochemical sensors have been developed based on polyethylene terephthalate track-etched membranes (PET TeMs) modified by photograft copolymerization of N-vinylformamide (N-VFA) and trimethylolpropane trimethacrylate (TMPTMA). The modification, structure and properties of the modified PET TeMs were thoroughly characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM), thermogravimetric analysis (TGA), Fourier-transform infrared (FTIR) spectroscopy, gas permeability measurements and contact angle analysis. Optimal membrane modification was achieved using C = 10% (N-VFA), 60 min of UV irradiation and a UV lamp distance of 10 cm. Furthermore, the modified membranes were implemented in a two-electrode configuration for the determination of Eu³⁺, Gd³⁺, La³⁺ and Ce³⁺ ions via square-wave anodic stripping voltammetry (SW-ASV). The sensors exhibited a linear detection range from 10⁻⁷ M to 10⁻³ M, with limits of detection of 1.0 × 10⁻⁶ M (Eu³⁺), 6.0 × 10⁻⁶ M (Gd³⁺), 2.0 × 10⁻⁴ M (La³⁺) and 2.5 × 10⁻⁵ M (Ce³⁺). The results demonstrated a significant enhancement in electrochemical response due to the grafted PET TeMs-g-N-PVFA-TMPTMA structure, and the sensor showed practical applicability and consistent performance in detecting rare earth ions in tap water. 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