Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.

Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.

Analysis on advanced transient EM detectability of coal mine roadway

The hidden water-bearing structures near the roadway tunnelling face are very likely to cause water seepage accidents in coal mines.Currently,transient electromagnetic(EM)technology has be-come an important method to detect water damage in advance of roadway excavation.In this paper,the time-domain finite element algorithm based on unstructured tetrahedron grids is used to accurate-ly simulate the geological body in front of the roadway excavation face and analyze its response.The authors detect the distance between the roadway excavation face and the low-resistivity water-bearing body,the resistivity difference between the low-resistivity body and surrounding rock,and the influence of the size of the low-resistivity body on the transient EM response.Furthermore,the common types of low-resistivity bodies in the roadway drivage process are used for modeling to analyze the attenuation of the detected EM response when there are low-resistivity bodies in front of the roadway.The research in this paper can help effectively detecting the water-bearing low-resistivity body in front of the roadway drivage and lay a foundation for reducing the risk of water seepage accidents.

3D finite element numerical simulation of advanced detection in roadway for DC focus method

Within the roadway advanced detection methods, DC resistivity method has an extensive application because of its simple principle and operation. Numerical simulation of the effect of focusing current on advanced detection was carried out using a three-dimensional finite element method （FEM）, meanwhile the electric-field distribution of the point source and nine-point power source were calculated and analyzed with the same electric charges. The results show that the nine-point power source array has a very good ability to focus, and the DC focus method can be used to predict the aquifer abnormality body precisely. By comparing the FEM modelling results with physical simulation results from soil sink, it is shown that the accuracy of forward simulation meets the requirement and the artificial disturbance from roadway has no impact on the DC focus method.

Experiment in metal disturbance during advanced detection using a transient electromagnetic method in coal mines

Tunneling machines, or excavators, are large and good conductors and affect the reliability of data gathering and interpretation in advanced detection using transient electromagnetic methods. In our experiment, we used a coincident-loop and central loop type of configuration, where the coil plane l） vertical to and 2） parallel to the working face. A SIROTEM instrument at different locations was used to observe the transient electromagnetic responses of the excavator and to analyze the response amplitudes. The result shows that the tunneling machine affects the advanced detection data and is related to the way the coil is coupled. When the excavator is 6 m from the observatory, the interference of tunneling machine can be ignored.

Experimental analysis on bolt interference during advanced water detection with the mine transient electromagnetic method

During advanced water detection using the transient electromagnetic method, the exploration effect for water-rich area is often poor due to the interference of bolts that are distributed in different positions in working face. Thus, the study on the interference characteristics of bolts in different states has important directive significance for improving the acquisition quality and data processing method in water detection. Based on the analysis of the distribution laws of magnetic field excited by small multi-turn coincident loop in full space of homogeneity, the test on the interference of bolts has been designed in the mine. Through drilling 18 holes around the overlapping coil in the working face, mass data are collected in order with the posi- tion change and the exposed bolt length. The results of comprehensive data analysis show that the transient electromagnetic field is strongly interfered as the distance between the bolt and the center of the coil is less than 3 m, and the interference varies greatly as the distance varies. On the other hand, the field induced by the bolts can be ignored as the distance exceeds 3 m. The findings can help to improve data acquisition and correction during advanced water detection when using the transient electromagnetic method.

Fine analysis on advanced detection of transient electromagnetic method

Fault fracture zones and water-bearing bodies in front of the driving head are the main disasters in mine laneways,thus it is important to perform their advanced detection and prediction in advance in order to provide reliable technical support for the excavation.Based on the electromagnetic induction theory,we analyzed the characteristics of primary and secondary fields with a positive and negative wave form of current,proposed the fine processing of the advanced detection with variation rate of apparent resistivity and introduced in detail the computational formulae and procedures.The result of physical simulation experiments illustrate that the tectonic interface of modules can be judged by first-order rate of apparent resistivity with a boundary error of 5%,and the position of water body determined by the fine analysis method agrees well with the result of borehole drilling.This shows that in terms of distinguishing structure and aqueous anomalies,the first-order rate of apparent resistivity is more sensitive than the secondorder rate of apparent resistivity.However,some remaining problems are suggested for future solutions.

Numerical simulations of full-wave fi elds and analysis of channel wave characteristics in 3-D coal mine roadway models

Currently, numerical simulations of seismic channel waves for the advance detection of geological structures in coal mine roadways focus mainly on modeling two- dimensional wave fields and therefore cannot accurately simulate three-dimensional （3-D） full-wave fields or seismic records in a full-space observation system. In this study, we use the first-order velocity-stress staggered-grid finite difference algorithm to simulate 3-D full-wave fields with P-wave sources in front of coal mine roadways. We determine the three components of velocity Vx, Vy, and Vz for the same node in 3-D staggered-grid finite difference models by calculating the average value of Vy, and Vz of the nodes around the same node. We ascertain the wave patterns and their propagation characteristics in both symmetrical and asymmetric coal mine roadway models. Our simulation results indicate that the Rayleigh channel wave is stronger than the Love channel wave in front of the roadway face. The reflected Rayleigh waves from the roadway face are concentrated in the coal seam, release less energy to the roof and floor, and propagate for a longer distance. There are surface waves and refraction head waves around the roadway. In the seismic records, the Rayleigh wave energy is stronger than that of the Love channel wave along coal walls of the roadway, and the interference of the head waves and surface waves with the Rayleigh channel wave is weaker than with the Love channel wave. It is thus difficult to identify the Love channel wave in the seismic records. Increasing the depth of the receivers in the coal walls can effectively weaken the interference of surface waves with the Rayleigh channel wave, but cannot weaken the interference of surface waves with the Love channel wave. Our research results also suggest that the Love channel wave, which is often used to detect geological structures in coal mine stopes, is not suitable for detecting geological structures in front of coal mine roadways. Instead, the Rayleigh channel wave can be used for the advance detection of geological structures in coal mine roadways.

Prediction Technology of Buried Water-Bearing Structures in Coal Mines Using Transient Electromagnetic Method

Buried water-conducting and water-bearing structures in front of the driving head may easily lead to water bursts in coal mines. Therefore,it is very important for the safety of production to make an accurate and timely forecast about water bursts. Based on the smoke ring effect of transient electromagnetic fields,the principle of transient electro-magnetic method used in detecting buried water-bearing structures in coal mines in advance,is discussed. Small multi-turn loop configurations used in coal mines are proposed and a field procedure of semicircular sector scanning is presented. The application of this method in one coal mine indicates that the technology has many advantages compared with others. The method is inexpensive,highly accurate and efficient. Suggestions are presented for future solutions to some remaining problems.

Research on application of transient electromagnetic technology in coal mine

Studied the principle of transient electromagnetic method in coalmine and solved the computation of the whole time apparent resistivity and the relation between apparent resistivity and exploration depth and so on. Studied the work method of transient electromagnetic method in coalmine and obtained reasonable arrangement way. Studied data processing and explanation method of transient electromagnetic method and obtained high quality electric section. Finally the purpose to detect water-bearing body and water-bearing structure in front of roadway in advance, and detect the water-bearing property of the roof and floor rock layer of coal face were realized by use of transient electromagnetic method.

Recent Advance in the Electrochemical Detection of Sulphide and Sulphhydryl Species

This article aims at providing a critical review of some most recent developments in the electrochemical detection and measurement of hydrogen sulphide and the related species, which are of great significance to a variety of industries and in environmental moitoring. The molecular recognition processes are initiated by using either an organic precursor or a catalytic complex, leading to extensive ranges of detection. A series of advanced chemical and simulation techniques are used to probe the mechanistic details of the analytical chemistry involved.

Acknowledgments

Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical

Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series

In the context of rapid digitization in industrial environments,how effective are advanced unsupervised learning models,particularly hybrid autoencoder models,at detecting anomalies in industrial control system(ICS)datasets?This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things(IoT)devices,which can significantly improve the reliability and safety of these systems.In this paper,we propose a hybrid autoencoder model,called ConvBiLSTMAE,which combines convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)to more effectively train complex temporal data patterns in anomaly detection.On the hardware-in-the-loopbased extended industrial control system dataset,the ConvBiLSTM-AE model demonstrated remarkable anomaly detection performance,achieving F1 scores of 0.78 and 0.41 for the first and second datasets,respectively.The results suggest that hybrid autoencoder models are not only viable,but potentially superior alternatives for unsupervised anomaly detection in complex industrial systems,offering a promising approach to improving their reliability and safety.