Search Results (9,514)

Monitoring nitrogen dioxide (NO₂) in various scenarios is crucial due to its significant environmental impact as a hazardous gas which is emitted by several industrial sectors. This study reports the optimized synthesis of WO₃ flower-like structures using the microwave-assisted hydrothermal method under various experimental conditions, resulting in the optimized sample designated MF-WO₃-K2. Structural, morphological, and chemical characterizations revealed that WO₃ microflowers (MF-WO₃-K2) exhibit a hexagonal crystalline phase, a bandgap of 2.4 eV, and a high specific surface area of 61 m²/g. The gas-sensing performance of WO₃ microflowers was investigated by electrical measurements of six similarly fabricated MF-WO₃-K2 sensors. The MF-WO₃-K2 sensors demonstrated a remarkable sensor signal of 225 for 5 ppm NO₂ at 150 °C and response/recovery times of 14.5/2.4 min, coupled with outstanding selectivity against potential interfering gases such as CO, H₂, C₂H₂, and C₂H₄. Additionally, the sensors achieved a low detection limit of 65 ppb for NO₂ at 150 °C. The exceptional sensing properties of WO₃ microflowers are attributed to the abundance of active sites on the surface, large specific surface area, and the presence of pores in the material that facilitate the diffusion of NO₂ molecules into the structure. Overall, the WO₃ microflowers demonstrate a promising ability to be used as a sensitive layer in high-performance chemiresistive gas sensors due to their high sensor performance and good reproducibility for NO₂ detection. Full article

WSN coverage optimization faces two key challenges: firstly, traditional algorithms are prone to getting stuck in local optima, leading to ‘coverage holes’ in node deployment; Secondly, in dynamic scenarios (such as imbalanced energy consumption of nodes), the convergence speed of the algorithm is slow, making it difficult to maintain high coverage in real time. This study focuses on the coverage optimization problem of wireless sensor networks (WSNs) and proposes improvements to the Flamingo Search Optimization Algorithm (FSA). Specifically, the algorithm is enhanced by integrating the elite opposition-based learning strategy and the stagewise step-size control strategy, which significantly improves its overall performance. Additionally, the introduction of a cosine variation factor combined with the stagewise step-size control strategy enables the algorithm to effectively break free from local optima constraints in the later stages of iteration. The improved Flamingo Algorithm is applied to optimize the deployment strategy of sensing nodes, thereby enhancing the coverage rate of the sensor network. First, an appropriate number of sensing nodes is selected according to the target area, and the population is initialized using a chaotic sequence. Subsequently, the improved Flamingo Algorithm is adopted to optimize and solve the coverage model, with the coverage rate as the fitness function and the coordinates of all randomly distributed sensing nodes as the initial foraging positions. Next, a search for candidate foraging sources is performed to obtain the coordinates of sensing nodes with higher fitness; the coordinate components of these candidate foraging sources are further optimized through chaos theory to derive the foraging source with the highest fitness. Finally, the coordinates of the optimal foraging source are output, which correspond to the coordinate values of all sensing nodes in the target area. Experimental results show that after 100 and 200 iterations, the coverage rate of the improved Flamingo Search Optimization Algorithm is 7.48% and 5.68% higher than that of the original FSA, respectively. Furthermore, the findings indicate that, by properly configuring the Flamingo population size and the number of iterations, the improved algorithm achieves a higher coverage rate compared to other benchmark algorithms. Full article

Reed sensors play an important role in improving the safety, reliability, and efficiency of modern electric vehicles. Our study evaluates their performance by measuring the switching distance under five different configurations of a cylindrical magnet using a 3D-printed test fixture. Statistical analysis revealed that the right-shift-upward configuration yielded the best performance, significantly reducing the release distance. Building on this, a prototype housing was developed using Selective Laser Sintering with polybutylene terephthalate, and a stainless-steel spring was incorporated to enhance sensitivity and reliability. The spring integration reduced the activation distance to 2.3 mm, which is an improvement of up to 60%, and it also significantly improved the consistency of the results. These outcomes demonstrate a practical method for manufacturing more reliable reed sensors for automotive sensing technology. Full article

This paper presents the design and analysis of a compact microstrip fixed-frequency double-inductive-coupled filter with selected band suppression. The filter can be used as an input filter in wireless IoT sensors. The proposed structure has reduced dimensions and improved out-of-band attenuation, achieved through the use of radial stub lines as elements of the resonators. These lines act as capacitors within the passband, while in a selected sub-band as series resonant circuits, effectively enhancing attenuation. The frequency response of the filter is shaped using two transmission zeros: the first one improves the steepness of the frequency response at the upper transition band, while the second increases attenuation in a chosen sub-band of the stopband. An analysis of the filter is presented, and key equations describing its properties are derived. An example filter for the frequency band 2.391–2.525 GHz, with additional suppression introduced in the U-NII 5 GHz band was designed, manufactured and examined. The insertion loss achieved by the proposed filter is lower than 1.6 dB, its attenuation across the whole stopband exceeds 30 dB and reaches over 40 dB in the 4.7–5.9 GHz frequency band. Full article

Accurately forecasting air quality could lead to the development of dynamic, data-driven policy-making and improved early warning detection systems. Deep learning has demonstrated the potential to produce highly accurate forecasting models, but it is noted that much literature focuses on narrow datasets and typically considers one geographic area. In this research, three diverse air quality datasets are utilised to evaluate four deep learning algorithms, which are feedforward neural networks, Long Short-Term Memory (LSTM) recurrent neural networks, DeepAR and Temporal Fusion Transformers (TFTs). The study uses these modules to forecast CO, NO₂, O₃, and particulate matter 2.5 and 10 (PM_2.5, PM₁₀) individually, producing a 24 h forecast for a given sensor and pollutant. Each model is optimised using a hyperparameter and a feature selection process, evaluating the utility of exogenous data such as meteorological data, including wind speed and temperature, along with the inclusion of other pollutants. The findings show that the TFT and DeepAR algorithms achieve superior performance over their simpler counterparts, though they may prove challenging in practical applications. It is noted that while some covariates such as CO are important covariates for predicting NO₂ across all three datasets, other parameters such as context length proved inconsistent across the three areas, suggesting that parameters such as context length are location and pollutant specific. Full article

Natural gas pipeline leak monitoring suffers from severe environmental noise, non-stationary signals, and complex multi-source variable couplings, limiting prediction accuracy and robustness. Inspired by biological perceptual systems, particularly their multimodal integration and dynamic attention allocation, we propose GL-TransLSTM, a biomimetic hybrid deep learning model. It synergistically combines Transformer’s global self-attention (emulating selective focus) and LSTM’s gated memory (mimicking neural temporal retention). The architecture incorporates a multimodal fusion pipeline; raw sensor data are first decomposed via CEEMDAN to extract multi-scale features, then processed by an enhanced LSTM-Transformer backbone. A novel physics-informed gated attention mechanism embeds gas diffusion dynamics into attention weights, while an adaptive sliding window adjusts temporal granularity. This study makes evaluations on an industrial dataset with methane concentration, temperature, and pressure, GL-TransLSTM achieves 99.93% accuracy, 99.86% recall, and 99.89% F1-score, thereby significantly outperforming conventional LSTM and Transformer-LSTM baselines. Experimental results demonstrate that the proposed biomimetic framework substantially enhances modeling capacity and generalization for non-stationary signals in noisy and complex industrial environments through multi-scale fusion, physics-guided learning, and bio-inspired architectural synergy. Full article

Exhaled breath analysis is emerging as one of the most promising non-invasive strategies for the early detection of life-threatening diseases, especially lung cancer, where rapid and reliable diagnosis remains a major clinical challenge. In this study, we designed and optimized an electronic nose (e-nose) platform composed of quantum resistive vapor sensors (vQRSs) engineered by polymer-carbon nanotube nanocomposites via spray layer-by-layer assembly. Each sensor was tailored through specific polymer functionalization to tune selectivity and enhance sensitivity toward volatile organic compounds (VOCs) of medical relevance. The sensor array, combined with linear discriminant analysis (LDA), demonstrated the ability to accurately discriminate between cancer-related biomarkers in synthetic blends, even when present at trace concentrations within complex volatile backgrounds. Beyond artificial mixtures, the system successfully distinguished real exhaled breath samples collected under challenging conditions, including before and after smoking and alcohol consumption. These results not only validate the robustness and reproducibility of the vQRS-based array but also highlight its potential as a versatile diagnostic tool. Overall, this work underscores the relevance of nanocomposite chemo-resistive arrays for breathomics and paves the way for their integration into future portable e-nose devices dedicated to telemedicine, continuous monitoring, and early-stage disease diagnosis. Full article

The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems. Full article

Tea is one of the most widely consumed beverages worldwide, yet increasing environmental cadmium (Cd²⁺) contamination poses a serious threat to consumer safety. Understanding the migration pathway of Cd²⁺ from contaminated soils through tea plants into brewed infusions is essential for comprehensive risk assessment across the entire tea supply chain. However, conventional analytical methods for Cd²⁺ detection are often time-consuming, labor-intensive, and unsuitable for rapid or on-site monitoring. In this study, we developed a facile, sensitive, and selective electrochemical sensing platform based on a Bi³⁺-rich metal–organic framework (MOF(Bi)) for reliable Cd²⁺ quantification in various tea-related matrices. The MOF(Bi) was synthesized via a solvothermal method and directly immobilized onto a glassy carbon electrode (GCE) in a one-step modification process. To enhance Cd²⁺ preconcentration, cysteine was introduced as a complexing agent, while Nafion was employed to stabilize the sensing interface and improve reproducibility. The resulting Nafion/cys/MOF(Bi)/GCE sensor exhibited excellent sensitivity with a wide linear range from 0.2 and 25 μg/L, a low detection limit of 0.18 μg/L (S/N = 3), high selectivity against common interfering ions, and good stability. This platform enabled accurate tracking of Cd²⁺ transfer from polluted garden soil to raw tea leaves and finally into tea infusions, showing strong correlation with ICP-MS results. Our strategy not only offers a practical tool for on-site food safety monitoring but also provides new insights into heavy metal transfer behavior during tea production and consumption. Full article

Self-driving vehicle (SDV) safety and reliability are becoming critical design parameters as SDVs increase their market share. This paper examines public preferences for key SDV safety features (system reliability, sensor resilience, failure behavior, and driver alert methods) using a choice-based conjoint survey of 403 U.S. respondents. A novel integration of conjoint analysis with Least Absolute Shrinkage and Selection Operator (LASSO) regression and generalized linear mixed-effects models (GLMMs) was applied to identify the most influential features and their demographic or behavioral predictors. Results show that multimodal driver alerts (i.e., audio + visual) were the most influential factor, accounting for nearly two-thirds of decision weight. System reliability (i.e., low human intervention rates) and sensor resilience (i.e., low tolerance for failures) were secondary, while failure behavior had minimal influence. Subgroup analyses revealed modest variations by willingness to pay for SDVs, income, race/ethnicity, marital status, education, driving frequency, and risk propensity, though the importance of alerts and reliability remained consistent across groups. This combined conjoint-LASSO-GLMM framework enhances the precision of preference estimation and offers actionable guidance for SDV manufacturers seeking to align safety feature design with consumer expectations. Full article

This study addresses developing systematic guidelines for the design of concentration control in the oxidation of benzene to maleic anhydride within a tubular reactor. The influence of step size selection and temperature sensor location on the tuning and performance of a PI/P cascade control system applied to the oxidation process was evaluated. The reactor’s dynamic behavior was analyzed using numerical simulations based on the solution of the Fortran mathematical model. Sensor positions and multiple step sizes (from +10% to −10%) were analyzed to characterize reactor dynamics and optimize control parameters. The results show that a controller design corresponding to a −9% step in the jacket temperature offered the best performance, ensuring process stability and selectivity. In contrast, step changes between +10% and −8% caused temperature deviations beyond safe limits. Since maleic anhydride is an essential precursor in the production of resins, plastics, lubricants, and pharmaceutical intermediates, optimizing the efficiency and safety of its production represents a significant benefit to the global chemical industry. Full article

Unsupervised medical image anomaly detection, which does not need any labels, holds a pivotal role in early disease detection for advancing human intelligent health, and it is among the prominent research endeavors in the realm of biomedical image analysis. Existing deep model-based methods mainly focus on feature selection and interaction, ignoring the relative position and shape uncertainty of the anomalies themselves, which play an important guiding role in disease diagnosis, hampering performance. To address this issue, our study introduces a novel and effective framework, termed CRTSC, which integrates a channel-wise recalibration module (CRM) along with the texture–structural consistency constraint (TSCC) for anomaly detection in medical chest images acquired from different sensors. Specifically, the CRM adjusts the weight of different medical image feature channels, which are used to establish spatial relationships among anomalous patterns, enhancing the network’s representation and generalization capabilities. The texture–structural consistency constraint is devoted to enhancing the anomaly’s structural (shape) definiteness via evaluating the loss function of similarity between two images and optimizing the model. The two collaborate in an end-to-end fashion to optimize and train the entire framework, thereby enabling anomaly detection in medical chest images. Extensive experiments conducted on the public ZhangLab and CheXpert datasets demonstrate that our method achieves a significant performance improvement compared with the state-of-the-art methods, offering a robust and generalizable solution for sensor-based medical imaging applications. Full article

The detection of volatile organic compounds (VOCs) at low operating temperatures is critical for public health, environmental monitoring, and industrial safety, yet it remains a significant challenge for conventional sensor technologies. Metal-organic frameworks (MOFs) have emerged as highly versatile precursors for creating advanced sensing materials. This review critically examines the transformation of MOFs into functional catalytic interfaces for low-temperature chemiresistive VOC sensing. We survey the key synthetic strategies, with a focus on controlled pyrolysis, that enable the conversion of insulating MOF precursors into semiconducting derivatives with tailored porosity, morphology, and catalytically active sites. This review establishes the crucial synthesis-structure-performance relationships that govern sensing behavior, analyzing how factors like calcination temperature and precursor composition dictate the final material’s properties. We delve into the underlying chemiresistive sensing mechanisms, supported by evidence from advanced characterization techniques such as in situ DRIFTS and density functional theory (DFT) calculations, which elucidate the role of oxygen vacancies and heterojunctions in enhancing low-temperature catalytic activity. A central focus is placed on the persistent challenges of achieving high selectivity and robust performance in complex, real-world environments. We critically evaluate and compare strategies to mitigate interference from confounding gases and ambient humidity, including intrinsic material design and extrinsic system-level solutions like sensor arrays coupled with machine learning. Finally, this review synthesizes the current state of the art, identifies key bottlenecks related to stability and scalability, and provides a forward-looking perspective on emerging frontiers, including novel device architectures and computational co-design, to guide the future development of practical MOF-derived VOC sensors. Full article

Remarkable advances in Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) made it one of the most effective gas-sensing techniques in terms of sensitivity and selectivity. Consequently, its range of possible applications is continuously expanding, but in some cases is still limited by the cost and/or size of the equipment needed to im-plement a complete QEPAS sensor. In particular, bulky and expensive lab instruments are often used to realize the electronic building blocks required by this technique, which prevents, for instance, integration of the system on board a drone. This work addresses this issue by presenting the development of compact electronic modules for a QEPAS sensor. A very low-noise, fully differential preamplifier for the quartz tuning fork, with digital output and programmable gain, has been designed and realized. A compact FPGA board hosts both an accurate function generation module, which synthesizes the signals needed to modulate the laser source, and an innovative lock-in amplifier based on the CORDIC algorithm. QEPAS sensors based on the designed electronics have been used for the detection of H₂O and CO₂ in ambient air, proving the full functionality of all the blocks. These results highlight the potential of compact electronics to promote portable and cost-effective QEPAS applications. Full article

Blood glucose monitoring is essential for the treatment of diabetes, a chronic disease that affects millions of people worldwide. Non-electrochemical blood glucose sensors often lack sensitivity and selectivity, especially in complex biological fluids, and are not suitable for wearable point-of-care devices. Electrochemical blood glucose sensors, on the other hand, are easy to handle, inexpensive, and offer high sensitivity and selectivity even in the presence of interfering molecules. They can also be seamlessly integrated into wearable devices. This review explores the key blood glucose technologies, emphasizing the operating principle and classification of electrochemical glucose sensors. It also highlights the role of functional solid–liquid interfaces in optimizing sensor performance. Recent developments in solid–liquid interfacial materials, including metal-based, metal oxide-based, carbon-based, nanoparticle-based, conductive polymer, and graphene-based interfaces, are systematically analyzed for their sensing potential. Furthermore, this review highlights existing patents, the evolving market landscape, and data from clinical studies that bridge the gap between laboratory research and commercial application. Finally, we present future perspectives and highlight the need for next-generation wearable and enzyme-free glucose sensors for continuous and non-invasive glucose monitoring. Full article