Search Results (1,071)

Recent progress in microfluidic technologies has led to the development of compact and highly efficient electrochemical platforms, including lab-on-a-chip (LoC) systems, that integrate multiple testing functions into a single, portable device. Combined with smartphone-based electrochemical devices, these systems enable rapid and accurate on-site detection of food contaminants, including pesticides, heavy metals, pathogens, and chemical additives at farms, markets, and processing facilities, significantly reducing the need for traditional laboratories. Smartphones improve the performance of these platforms by providing computational power, wireless connectivity, and high-resolution imaging, making them ideal for in-field food safety testing with minimal sample and reagent requirements. At the core of these systems are electrochemical biosensors, which convert specific biochemical reactions into electrical signals, ensuring highly sensitive and selective detection. Advanced nanomaterials and integration with Internet of Things (IoT) technologies have further improved performance, delivering cost-effective, user-friendly food monitoring solutions that meet regulatory safety and quality standards. Analytical techniques such as voltammetry, amperometry, and impedance spectroscopy increase accuracy even in complex food samples. Moreover, low-cost engineering, artificial intelligence (AI), and nanotechnology enhance the sensitivity, affordability, and data analysis capabilities of smartphone-integrated electrochemical devices, facilitating their deployment for on-site monitoring of food and agricultural contaminants. This review explains how these technologies address global food safety challenges through rapid, reliable, and portable detection, supporting food quality, sustainability, and public health. Full article

Rapid on-site detection of chemical warfare agents (CWAs) is vital for security and environmental monitoring. In this work, copper(I) oxide (Cu₂O) nanowire (NW) chemiresistors were investigated as gas sensors for low-concentration organophosphorus chemical warfare agent (CWA) simulants. The NWs were hydrothermally synthesized and deposited onto microheater platforms, enabling them to operate at elevated working temperatures. Their sensing performance was tested against a range of vapor-phase simulants, including dimethyl methylphosphonate (DMMP), triethyl phosphate (TEP), diethyl ethylphosphonate (DEEP), diphenyl phosphoryl chloride (DPPCl), parathion, diethyl phosphite (DEP), diethyl adipate (DEA), and cyanogen chloride (ClCN). Fully oxidized P(V) simulants (DMMP, DEEP, TEP) produced modest, predominantly reversible responses (~3–6% RR). On the contrary, DPPCl and DEP induced the strongest relative responses (RR −94.67% and >200%, respectively), accompanied by irreversible surface modification as revealed by SEM and EDS. ClCN produced a substantial but reversible negative response (RR −9.5%), consistent with transient oxidative interactions. Surface poisoning was confirmed after exposure to DEP and DPPCl, which left phosphorus or chlorine residues on the Cu₂O surface. These results highlight both the promise and limitations of Cu₂O NW chemiresistors for selective CWA detection. Full article

Odorous emissions from hot-mix asphalt (HMA) plants are a growing environmental concern, particularly due to airborne aldehydes and ketones, which have low odor thresholds and a strong sensory impact. This study presents a field-ready analytical method for monitoring odor-active volatile compounds. The system uses dynamic solid-phase microextraction (SPME and SPME Arrow) with on-fiber derivatization via O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) and is coupled to gas chromatography–mass spectrometry (GC–MS) for direct detection. A flow-cell sampling unit enables the real-time capture of aliphatic aldehydes and ketones under transient emission conditions. Calibration using permeation tubes demonstrated sensitivity (limits of detection (LODs) below 0.13 μg/m³), recovery above 85% and consistent reproducibility. Compound identity was confirmed using retention indices and fragmentation patterns. Uncertainty assessment followed ISO GUM (Guide to the Expression of Uncertainty in Measurement) standards, thereby validating the method’s environmental applicability. Field deployment 200 m from an HMA facility identified measurable concentrations that aligned with CALPUFF model predictions. The method’s dual-isomer resolution and 10 min runtime make it ideal for responding to time-sensitive odor complaints. Overall, this approach supports regulatory efforts by enabling high-throughput on-site chemical monitoring and improving source attribution in cases of odor nuisance. Full article

The development of unconventional oil and gas resources highly relies on hydraulic fracturing technology, and the fracturing effect directly affects the level of oil and gas recovery. Carrying out fracturing evaluation is the main way to understand the fracturing effect. However, the current fracturing evaluation methods are usually carried out after the completion of fracturing operations, making it difficult to achieve real-time monitoring and dynamic regulation of the fracturing process. In order to solve this problem, an intelligent prediction method for fracture propagation based on the attention mechanism and Long Short-Term Memory (LSTM) neural network was proposed to improve the fracturing effect. Firstly, the GOHFER software was used to simulate the fracturing process to generate 12,000 groups of fracture geometric parameters. Then, through parameter sensitivity analysis, the key factors affecting fracture geometric parameters are identified. Next, the time-series data generated during the fracturing process were collected. Missing values were filled using the K-nearest neighbor algorithm. Outliers were identified by applying the 3-sigma method. Features were combined through the binomial feature transformation method. The wavelet transform method was adopted to extract the time-series features of the data. Subsequently, an LSTM model integrated with an attention mechanism was constructed, and it was trained using the fracture geometric parameters generated by GOHFER software, forming a surrogate model for fracture propagation. Finally, the surrogate model was applied to an actual fracturing well in Block Ma 2 of the Mabei Oilfield to verify the model performance. The results show that by correlating the pumping process with the fracture propagation process, the model achieves the prediction of changes in fracture geometric parameters and Stimulated Reservoir Volume (SRV) throughout the entire fracturing process. The model’s prediction accuracy exceeds 75%, and its response time is less than 0.1 s, which is more than 1000 times faster than that of GOHFER software. The model can accurately capture the dynamic propagation of fractures during fracturing operations, providing reliable guidance and decision-making basis for on-site fracturing operations. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation. Full article

The active pile underpinning technology when a tunnel passes under a bridge involves complex force conditions, making construction monitoring and control extremely challenging. However, there is a lack of research on the laws governing the stress and deformation responses of bridges during the construction process. This paper takes an active pile underpinning project of a metro line passing under a bridge as a case study. Design and construction plans are taken as the basis, and on-site monitoring data are incorporated. A three-dimensional finite element simulation model is established. This model is used to analyze the distribution and variation laws of stress and settlement during the pile underpinning process. The results show that: considering the traffic conditions of the bridge and the requirements for additional stress, it is reasonable to suggest that the actual settlement of the bridge deck should be 2–3 mm; the determination of the jacking force should generally be greater than the load transmitted from the pier column to the underpinning beam and less than 75% of the maximum bearing capacity, which is more reasonable. Full article

Antimicrobial resistance (AMR) is a significant global threat and is driven by the overuse of antibiotics in both clinical and agricultural settings. This issue is further complicated by the lack of rapid surveillance tools to detect resistant bacteria in clinical, environmental, and food systems. Of particular concern is the rise in resistance to carbapenems, a critical class of beta-lactam antibiotics. Rapid detection methods are necessary for prevention and surveillance effort. This study utilized a gold nanoparticle-based plasmonic biosensor to detect three CR genes: bla_KPC-3, bla_NDM-1, and bla_OXA-1. Optical signals were analyzed using both a spectrophotometer and a smartphone app that quantified visual color changes using RGB values. This app, combined with a simple boiling method for DNA extraction and a portable thermal cycler, was used to evaluate the biosensor’s potential for POC use. Advantages of the portable bacterial detection device include real time monitoring for immediate decision-making in critical situations, field and on-site testing in resource-limited settings without needing to transport samples to a centralized lab, minimal training required, automatic data analysis, storage and sharing, and reduced operational cost. Bacteria were inoculated into sterile water, river water, and turkey rinse water samples to determine the biosensor’s success in detecting target genes from sample matrices. Magnetic nanoparticles were used to capture and concentrate bacteria to avoid time-consuming cultivation and separation steps. The biosensor successfully detected the target CR genes in all tested samples using three gene-specific DNA probes. Target genes were detected with a limit of detection of 2.5 ng/L or less, corresponding to ~10³ CFU/mL of bacteria. Full article

The persistent presence of pesticide residues in vegetables raises significant concerns for food safety and public health, highlighting the need for sensing platforms that are efficient, affordable, and environmentally friendly while minimizing analysis time and reagent use. In this study, we developed a laser-induced graphene electrode (LIGE) modified with a titanium dioxide–Enteromorpha-derived carbon composite (TiO₂@EDC) for the sensitive electrochemical detection of the herbicide diuron in vegetables. This integrated system streamlines material synthesis, electrode fabrication, and electrochemical analysis into a single, practical platform for food safety monitoring. Under optimized conditions, this sensor exhibited a wide linear detection range of 0.01 µM to 1 mM, with a low limit of detection of 2.99 nM (3 S/N) and a limit of quantification of 9.98 nM (10 S/N). Notably, the sensor demonstrated excellent analytical performance in real vegetable samples by accurately quantifying diuron residues in lettuce, indicating its potential for on-site monitoring of pesticide contamination in food matrices to ensure food safety. Full article

In the early faults of transformer windings, there are obvious variation characteristics of the spatial leakage magnetic field. Taking the leakage magnetic field as the fault characteristic quantity can establish an active defense system for transformer defects and faults, thereby increasing the service life of the equipment. However, the installation method of the optical fiber leakage magnetic field sensor, the principle of leakage magnetic field protection, the research and development of the protection device, and the dynamic model testing of the protection device are all key links in realizing the leakage magnetic field monitoring and active defense system. This paper first analyzes the symmetry of the winding leakage magnetic field, proposes invasive and non-invasive installation methods for optical fiber sensors based on different application scenarios, presents the principle of leakage magnetic field differential protection, and develops a protection device. The feasibility of the protection scheme proposed in this paper was verified through dynamic model experiments, and the early fault active defense system was put into actual on-site operation. Full article

The increasing adoption of industrialized offsite construction (IOC) offers substantial benefits in efficiency, quality, and sustainability, yet presents persistent challenges related to data fragmentation, real-time monitoring, and coordination. This systematic review investigates the transformative role of artificial intelligence (AI)-enhanced digital twins (DTs) in addressing these challenges within IOC. Employing a hybrid re-view methodology—combining scientometric mapping and qualitative content analysis—52 relevant studies were analyzed to identify technological trends, implementation barriers, and emerging research themes. The findings reveal that AI-driven DTs enable dynamic scheduling, predictive maintenance, real-time quality control, and sustainable lifecycle management across all IOC phases. Seven thematic application clusters are identified, including logistics optimization, safety management, and data interoperability, supported by a layered architectural framework and key enabling technologies. This study contributes to the literature by providing an early synthesis that integrates technical, organizational, and strategic dimensions of AI-driven DT implementation in IOC context. It distinguishes DT applications in IOC from those in onsite construction and expands AI’s role beyond conventional data analytics toward agentive, autonomous decision-making. The proposed future research agenda offers strategic directions such as the development of DT maturity models, lifecycle-spanning integration strategies, scalable AI agent systems, and cost-effective DT solutions for small and medium enterprises. Full article

This study evaluates a solar-powered electric mobility pilot implemented at the University of Cuenca (Ecuador), combining two electric vans with daytime charging from a 35 kWp PV microgrid. Real-world monitoring with SCADA covered one year of operation, including efficiency tests across urban, highway, and mountainous routes. Over the monitored period, the fleet completed 5256 km in 1384 trips with an average occupancy of approximately 87%. Energy use averaged 0.17 kWh/km, totaling 893.52 kWh, of which about 98.2% came directly from on-site PV generation; only 2.41% of the annual PV output was required for vehicle charging. This avoided 1310.52 kg of CO₂ emissions compared to conventional vehicles. Operating costs were reduced by institutional electricity tariffs (0.065 USD/kWh) and the absence of additional PV investment, with estimated savings of around USD 2432 per vehicle annually. Practical guidance from the pilot includes aligning fleet schedules with peak solar generation, ensuring access to slow daytime charging points, maintaining high occupancy through route management, and using basic monitoring to verify performance. These results confirm the technical feasibility, economic competitiveness, and replicability of solar-electric transport in institutional settings with suitable solar resources and infrastructure. Full article

To achieve dual functionality that can monitor both Al³⁺ levels in food and the freshness of fish, rice straw fibers (RSFs) were treated in NaOH solutions and then cationized with 2,3-epoxypropyltrimethylammonium chloride, onto which alizarin red S molecules were immobilized through electrostatic interaction to develop a smart felt-like label. An optimized treatment in 5 wt% NaOH solution effectively removed lignin and hemicellulose, facilitating quaternary ammonium group grafting and stable ARS anchoring. The ARS@BRSF-5NaOH exhibited high pH sensitivity, showing visually discernible color changes (ΔE > 5, perceptible to the naked eye) under acidic (pH ≤ 6) and strongly alkaline (pH > 12) conditions. During the storage of the fish, the label transformed from yellow to dark purple (ΔE increase) as TVB-N levels approached 20 mg/100 g, enabling real-time freshness monitoring for protein-rich products. Additionally, the label achieved a detection threshold of 1 × 10⁻⁵ mol·L⁻¹ for Al³⁺ through a coordination-induced chromatic transition (purple to pale pink). This research highlights the feasibility of utilizing an agricultural waste-derived material to develop cost-effective, visually responsive, dual-functional intelligent labels for food safety, offering significant advancements in on-site quality assessment. Full article

There is currently no mature calculation theory to accurately predict the settlement of high-fill subgrade. This paper developed an intelligent monitoring system to accurately predict the settlement of high-fill subgrade based on on-site experiments, and the back-propagation (BP) neural network model was used to predict the settlement of high-fill subgrade. The results show that multiple data preprocessing methods built into intelligent systems can automatically generate multi-point and correlation curves, and the system can identify and distinguish various influencing factors to improve the accuracy and reliability of monitoring data. There will be a certain initial settlement of subgrade in the initial stage after filling construction is completed, and the settlement rate at this stage is relatively fast. Afterwards, the soil enters a rapid consolidation stage, and the settlement rate of subgrade gradually slows down. Finally, the filling soil consolidation becomes stable, and the rate of subgrade settlement enters a relatively stable stage. In addition, the BP neural network model is a good method for predicting the settlement of high-fill subgrade. The research findings can provide inspiration for developing an intelligent monitoring system to accurately predict the settlement of high-fill subgrade. Full article

Earth Pressure Balance (EPB) shield machines have been widely used in subway construction due to their versatility and safety. During the shield tunneling process, the earth pressure in the shield machine chamber is crucial for controlling ground settlement and ensuring the safety of surrounding buildings. However, current research on the temporal and spatial evolution of earth pressure in water-rich sand layers and its relationship with ground settlement is relatively insufficient. This study focuses on the shield tunneling project between Liuzhou East Road and Puzhou Road on Nanjing Metro Line 11. First, laboratory and on-site tests were conducted to optimize the slump properties of the sediment. Then, based on Terzaghi’s theory and statistical methods, the temporal and spatial evolution trends of the earth pressure in the shield chamber under water-rich sand conditions were explored. Finally, by adjusting earth pressure control parameters on-site and monitoring ground settlement, the impact of earth pressure changes on ground settlement was analyzed. Results showed a linear correlation between the actual earth pressure and shield burial depth. For water-rich sand with medium permeability, the theoretical earth pressure was calculated using Terzaghi’s water-soil combined method in shallow sections, and the average of combined and separated methods in deep sections. The decay envelope showed an exponential downward trend, with rapid decay initially and slower decay later. As earth pressure control values increased, pre-consolidation settlement increased, instantaneous settlement decreased, pre-consolidation settlement rate slightly increased, and instantaneous settlement rate decreased. When excavation pressure was below theoretical pressure, higher instantaneous settlement rates could threaten surface structures. This research offers vital theoretical and data references for shield tunneling in water-rich sand layers and supports related EPB shield machine theory studies. Full article