Tight sandstones have low porosity and permeability and strong heterogeneities with microcracks, resulting in small wave impedance contrasts with the surrounding rock and weak fluid-induced seismic effects, which make the seismic characterization for fluid detection and identification difficult. For this purpose, we propose a reformulated modified frame squirt-flow (MFS) model to describe wave attenuation and velocity dispersion. The squirt-flow length (R) is an important parameter of the model, and, at present, no direct method has been reported to determine it. We obtain the crack properties and R based on the DZ (David-Zimmerman) model and MFS model, and how these properties affect the wave propagation, considering ultrasonic experimental data of the Sichuan Basin. The new model can be useful in practical applications related to exploration areas.
Objective The aim of this study was to establish the effects of simultaneous and asynchronous masking on the detection and identification of visual and auditory alarms in close temporal proximity. Background In complex and highly coupled systems, malfunctions can trigger numerous alarms within a short period of time. During such alarm floods, operators may fail to detect and identify alarms due to asynchronous and simultaneous masking. To date, the effects of masking on detection and identification have been studied almost exclusively for two alarms during single-task performance. This research examines 1) how masking affects alarm detection and identification in multitask environments and 2) whether those effects increase as a function of the number of alarms. Method Two experiments were conducted using a simulation of a drone-based package delivery service. Participants were required to ensure package delivery and respond to visual and auditory alarms associated with eight drones. The alarms were presented at various stimulus onset asynchronies (SOAs). The dependent measures included alarm detection rate, identification accuracy, and response time. Results Masking was observed intramodally and cross-modally for visual and auditory alarms. The SOAs at which asynchronous masking occurred were longer than reported in basic research on masking. The effects of asynchronous and, even more so, simultaneous masking became stronger as the number of alarms increased. Conclusion Masking can lead to breakdowns in the detection and identification of alarms in close temporal proximity in complex data-rich domains. Application The findings from this research provide guidance for the design of alarm systems.
Laser-induced breakdown spectroscopy (LIBS), which has recently emerged as tool for geochemical analysis outside the traditional laboratory setting, is an ideal tool for Li exploration because it is the only technique that can measure Li in minerals, rocks, soils, and brines in-situ in the field. In addition to being used in many products essential to modern life, Li is a necessary element for a reduced carbon future and Li–Cs–Ta (LCT) granitic pegmatites are an important source of Li. Such pegmatites can have varying degrees of enrichment in Li, Rb, Cs, Be, Sn, Ga, Ta>Nb, B, P, and F. We focus here on the LCT pegmatites of the Carolina Tin-Spodumene Belt (CTSB) situated in the Kings Mountain Shear Zone, which extends from South Carolina into North Carolina. The CTSB hosts both barren and fertile pegmatites, with Li-enriched pegmatites containing spodumene, K-feldspar, albite, quartz, muscovite, and beryl. We illustrate how handheld LIBS analysis can be used for real-time Li analysis in the field at a historically important CTSB pegmatite locality in Gaston County, N.C. in four contexts: (i) elemental detection and identification; (ii) microchemical mapping; (iii) depth profiling; and (iv) elemental quantitative analysis. Finally, as an example of a practical exploration application, we describe how handheld LIBS can be used to measure K/Rb ratios and Li contents of muscovite and rapidly determine the degree of pegmatite fractionation. This study demonstrates the potential of handheld LIBS to drastically reduce the time necessary to acquire geochemical data relevant to acquiring compositional information for pegmatites during a Li pegmatite exploration program.
In this study, the aim was to investigate the discriminatory power of molecular diagnostics based on mNGS and traditional 16S ribosomal RNA PCR among Nocardia species. A total of fourteen clinical isolates from patients with positive Nocardia cultures and clinical evidence were included between January 2017 and June 2020 in HeNan Provincial People’s Hospital. DNA extraction and 16S rRNA PCR were performed on positive cultures, and pathogens were detected by mNGS in these same samples directly. Among the 14 Nocardia isolates, four species were identified, and N. cyriacigeorgica (8 cases) is the most common species. Twelve of the 14 Nocardia spp. isolates were identified by the two methods, while two strains of N. cyriacigeorgica were not identified by mNGS. All tested isolates showed susceptibility to trimethoprim-sulfamethoxazole (SXT), amikacin and linezolid. Apart from Nocardia species, other pathogens such as Acinetobacter baumannii, Klebsiella pneumonia, Aspergillus, Enterococcus faecalis, Human herpesvirus, etc., were detected from the same clinical samples by mNGS. However, these different pathogens were considered as colonization or contamination. We found that it is essential to accurately identify species for determining antibiotic sensitivity and, consequently, choosing antibiotic treatment. 16S rRNA PCR was useful for identification of nocardial infection among species, while this technique needs the clinicians to make the pre-considerations of nocardiosis. However, mNGS may be a putative tool for rapid and accurate detection and identification of Nocardia, beneficial for applications of antimicrobial drugs and timely adjustments of medication.
With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.
Information data protection is necessary to ward off and overcome various fraud attacks that may be encountered. A secret sharing scheme that implements cryptographic methods intends to maintain the security of confidential data by a group of trusted parties is the answer. In this paper, we choose the application of recursive algorithm on Shamir-based linear scheme as the primary method. In the secret reconstruction stage and since the beginning of the share distribution stage, these algorithms have been integrated by relying on a detection parameter to ensure that the secret value sought is valid. Although the obtained scheme will be much simpler because it utilizes the Vandermonde matrix structure, the security aspect of this scheme is not reduced. Indeed, it is supported by two detection parameters formulated from a recursive algorithm to detect cheating and identify the cheater(s). Therefore, this scheme is guaranteed to be unconditionally secure and has a high time efficiency (polynomial running time).