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Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. [ABSTRACT FROM AUTHOR]

Objectives: In view of the problem that it is difficult to identify the high hidden landslides by traditional ground investigation methods, the author's team have proposed the space-air-ground investigation system of landslide detection and monitoring. It has been applied nationwide with good results. However, there are few academic reports about it. In the study area of Danba County, where landslide disasters are frequent. Therefore, the detection and monitoring of potential landslides based on the space-air-ground investigation system was carried out. Methods: First, stacking-interferometric synthetic aperture radar(StackingInSAR) and small baseline subsets-InSAR(SBAS-InSAR) were combined with high-resolution optical remote sensing to achieve a wide-area census of active landslides in the whole study area, and the results of Stacking-InSAR and SBAS-InSAR were compared and analyzed. Second, light detection and ranging (LiDAR) technology and unmanned aerial vehicle aerial photography technology were used to conduct a detailed investigation of landslides in high-risk areas around Danba County, and further verification of major landslides through on-site investigation. Finally, SBAS-InSAR technology was used to monitor and analyze the deformation of typical landslides. Results: 41 active landslides were detected in the study area by using the space-air-ground investigation system. They are mainly local resurgence of large ancient land-slide accumulations under long-term gravity, river scouring and human engineering activities, etc. Most of the landslide deformations have a good correlation with rainfall. Conclusions: In the process of identifying landslides by using the space-air-ground investigation system, it is necessary to give full play to the advantages of each technical means. [ABSTRACT FROM AUTHOR]

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations’sustainable development goals.

As a common bulking agent and stabilizer for residents, aluminum-containing food additives have a comprehensive application scenario. However, it is noteworthy that the abuse of aluminum-containing food additives may cause a potential health risk to the public. Taking aluminum-containing food additives as the research object, the paper systematically discusses the basic characteristics of aluminum-containing food additives, detection technology of aluminum-containing food additives, and monitoring of food aluminum content, and summarizes the current problems for each part.

In this research work, coronavirus disease 2019 (COVID-19) has been considered to help mankind survive the present-day pandemic. This research is helpful to monitor the patients newly infected by the virus, and patients who have already recovered from the disease, and also to study the flow of virus from similar health issues. In this paper, an internet of things (IoT) framework has been developed for the early detection of suspected cases. This framework is used for collecting and uploading symptoms (data) through sensor devices to the physician, data analytics center, cloud, and isolation/health centers. The symptoms of the first wave, second wave, and omicron are used to identify the suspects. Five machine learning algorithms which are considered to be the best in the existing literature have been used to find the best machine learning classifier in this research work. The proposed framework is used for the rapid detection of COVID-19 cases from real-world COVID-19 symptoms to mitigate the spread in society. This model also monitors the affected patient who has undergone treatment and recovered. It also collects data for analysis to perform further improvements in algorithms based on daily updated information from patients to provide better solutions to mankind. [ABSTRACT FROM AUTHOR]

Large-scale production, rapid consumption, insufficient recovery and management, and slow degradation lead to a large accumulation of plastic waste and microplastics. Microplastics are characterized as stable, small, and having a large specific surface area and strong hydrophobicity. They are carriers of many hydrophobic organic pollutants, heavy metals, pathogenic bacteria and drug resistance genes. Worldwide, microplastic pollution in soils has attracted much attention. The progress and perspectives in the separation and detection of soil microplastics deserve a comprehensive review and discussion. Here, the sources and distributions of microplastics in soil from the use of agricultural plastic film, sludge recycling, long-term application of organic fertilizer, surface runoff, and sewage irrigation are summarized. Physical separation methods such as density separation, electrostatic separation, oil extraction and pressurized liquid extraction, and chemical extraction methods such as acid digestion, alkaline digestion, hydrogen peroxide and Fenton reagent oxidation, and enzymatic hydrolysis for soil microplastics are reviewed. Futhermore, the detection technologies of soil microplastics through microscopy, spectroscopy, mass spectrometry, thermogravimetric analysis, differential scanning calorimetry, X-ray photoelectron spectroscopy and nuclear magnetic resonance are reviewed. Finally, the perspectives are put forward from understanding the impacts of microplastics on soil functions and health, developing source control and environmental remediation technology, investigating low-cost and rapid separation and extraction methods that preserve the characteristics of microplastics, strengthen the degree of automation to avoid artificial operation error, and establish the standard methods for isolating, extracting, identifying, and quantifying microplastics in soils. This review serves as a technical reference for rapid identification of soil microplastics and builds the foundation for scientific assessment of the ecological and human environmental risks of soil microplastics.

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations'sustainable development goals. [ABSTRACT FROM AUTHOR]

The present study deals with non-destructive conventional emission, constant-wavelength synchronous fluorescence spectroscopy (CWSFS) and first derivative of CWSFS for selective estimation of two fluorescent xanthene dyes viz. rhodamine B (RhB) and fluorescein (Fls) in their binary and multifluorophoric mixtures via spike method in an aqueous environment. Various parameters (Δλ, pH, ionic strength, etc.) affecting these methods were optimized. Based on the results, measurement of RhB and Fls were performed at Δλ, 20.0 nm, pH 7.0, 25.0 ⁰C temperature and scan rate of 240.0 nm/min without their prior separation in mixtures. CWSFS and first derivative CWSFS measurements were carried out at 556.0 nm and 547.0 nm for RhB (R2 = 0.988 and 0.983) and 493.0 nm and 484.0 nm for Fls (R2 = 0.999 and 0.999) over the concentration range of both dyes at 0.10–0.70 mg/L. The mean percentage recoveries from aqueous binary mixtures of RhB and Fls were found to be 98.95, 99.97 (RhB) and 100.68, 100.73 (Fls) for CWSFS and first derivative CWSFS respectively. Results were statistically analyzed in terms of accuracy and precision.

Purposes of this research are introduction acceptable index for detection of dust storms and also identifying sources and pathways affected by this phenomenon in the East of Iran by studying a the dust storm using MODIS satellite images. For this purpose were used dust storm hourly data, satellite images of Modis and also digital and visual indexes including NDDI, BTDI, BTD, LRDI and false color composing for identifying dust storms. Finally, the studied dust storms were monitored using satellite images. Results indicated that LRDI index and false color composing have the higher resolution than the other indexes for detection of dust storms. Results monitoring dust storm event in the eastern Iran in summer indicated that storms sources of east area are dry beds of Hamoon lakes, Rigistan, Margo and Khas deserts at the convergence of the borders of Iran, Afghanistan and Pakistan and central Iran. The results of this research can be used in identifying of dust sources and pathways affected from this phenomenon by organizations and institutions to national and international planning.

With the rapid growth of China’s high-speed railway mileage, relevant safety technologies of railway operation are very urgent at present. One of the crucial parts is the pantograph–catenary system, which is directly related to the power supply safety of trains in high-speed railways. To ensure the safe operation of the pantograph–catenary system and eliminate the hidden danger during the power supply of trains, from 2011 in China, an automatic detection and monitoring system (called 6C system) of pantograph–catenary in high-speed railways was gradually established to timely discover and report all the potential defects and evaluate the performance of the pantograph–catenary system. The 6C system can measure the dynamic and static geometry parameters of the catenary and detect the defective working states of components and the operation conditions of the pantograph–catenary system. Considering the feedbacks from railway administrations, the 6C system can also realize a fine-grained detection and diagnosis of the pantograph–catenary and significantly improve the detection efficiency. In this article, the whole architecture of the 6C system is presented in detail, including six relatively independent parts for the detection and monitoring of pantograph–catenary systems and a data processing center with a three-level structure. Especially, some critical technologies for automatic detection and monitoring of pantograph–catenary systems are introduced. In the end, some new techniques of detection and monitoring are discussed, and suggestions are made for the future development of detection and monitoring systems of pantograph–catenary in high-speed railways. [ABSTRACT FROM AUTHOR]

There is a growing literature in industrial organization on the use of empirical screens to detect cartels. I discuss several methodological issues that have emerged from this literature. I explain why addressing these issues is important in gaining a better understanding of the power and limitations of empirical screens and for extending the retrospective application of empirical screens to dynamic, real-time monitoring of the market. I then highlight the intersections and discuss lessons and experiences from other literatures that are helpful in the design and use of empirical screens for both screening and monitoring. Future research topics are suggested. [ABSTRACT FROM AUTHOR]

Carbon capture and storage in sub-seabed geological formations (sub-seabed CCS) is currently being studied as a realistic option to mitigate the accumulation of anthropogenic CO 2 in the atmosphere. In implementing sub-seabed CCS, detecting and monitoring the impact of the sequestered CO 2 on the ocean environment is highly important. The first controlled CO 2 release experiment, Quantifying and Monitoring Potential Ecosystem Impacts of Geological Carbon Storage (QICS), took place in Ardmucknish Bay, Oban, in May–September 2012. We applied the in situ pH/ p CO 2 sensor to the QICS experiment for detection and monitoring of leaked CO 2 , and carried out several observations. The cabled real-time sensor was deployed close to the CO 2 leakage (bubbling) area, and the fluctuations of in situ pH and pCO 2 above the seafloor were monitored in a land-based container. The long-term sensor was placed on seafloor in three different observation zones. The sediment pH sensor was inserted into the sediment at a depth of 50 cm beneath the seafloor near the CO 2 leakage area. Wide-area mapping surveys of pH and pCO 2 in water column around the CO 2 leakage area were carried out by using an autonomous underwater vehicle (AUV) installed with sensors. Atmospheric CO 2 above the leakage area was observed by using a CO 2 analyzer that was attached to the bow of ship of 50 cm above the sea-surface. The behavior of the leaked CO 2 is highly dependent on the tidal periodicity (low tide or high tide) during the CO 2 gas release period. At low tide, the pH in sediment and overlying seawater decreased due to strong eruption of CO 2 gas bubbles, and the CO 2 ascended to sea-surface quickly with a little dissolution to seawater and dispersed into the atmosphere. On the other hand, the CO 2 bubbles release was lower at high tide due to higher water pressure, and slight low pH seawater and high atmospheric CO 2 were detected. After stopping CO 2 gas injection, no remarkable variations of pH in sediment and overlying water column were observed for three months. [ABSTRACT FROM AUTHOR]