Browse Articles

Interpretable real-time monitoring of short-term rockbursts in underground spaces based on microseismic activities

Application of big data technology in enterprise information security management

When curiosity gaps backfire: effects of headline concreteness on information selection decisions

Abstract The selection of information by individuals is a basic process in democratic institutions, including journalism. Publishers attempt to attract readers with “curiosity gap” headlines that offer vague descriptions rather than summarize an article. Lab and field experiments that compare the influence of these two styles have found conflicting results on their efficacy. In this registered report, we propose a theory, based on the psychology of curiosity, to harmonize these results. We introduce and validate an automated linear scale of headline concreteness to differentiate summary and curiosity gap headlines. In a meta-analysis of 8977 headline experiments, we confirm that the effects of headline concreteness on clickthrough rates vary with the overall concreteness of other headlines. When the baseline headline is too vague, higher headline concreteness increases clickthrough rates. When headlines are too concrete, higher headline concreteness decreases clickthrough rates. These findings suggest a curvilinear relationship between information selection decisions and the amount of information conveyed in text, implying that headlines that convey just the right amount of information maximize clickthrough rates at scale. Protocol registration The protocol for this study was pre-registered following in-principle acceptance at https://osf.io/fbzvw/ on September 21st, 2023. The stage 1 protocol for this Registered Report was accepted in principle on 30/08/23. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/FBZVW.

Trichostatin A promotes de novo shoot regeneration from Arabidopsis root explants via a cytokinin related pathway

Hand grip strength and quality of life among adults aged 50–90 years from South West Poland

Abnormal ac4C modification in metabolic dysfunction associated steatotic liver cells

College major decision making behavior of urban and rural students under cultural capital impact in China

Effect of dietary probiotics intake on cancer mortality: a cohort study of NHANES 1999–2018

Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study

Association between metabolic dysfunction associated steatotic liver disease and gallstones in the US population using propensity score matching

Single-Cell RNA sequencing reveals mitochondrial dysfunction in microtia chondrocytes

Enterprise chart question and answer method based on multi modal cross fusion

Outcomes and predictive factors for fluid resolution following three loading injections of faricimab for treatment-naïve neovascular age-related macular degeneration

An intelligent hybrid approach combining fuzzy C-means and the sperm whale algorithm for cyber attack detection in IoT networks

AbstractThe Internet of Things (IoTs) has revolutionized cities, enabling them to become smarter. IoTs play an important role in monitoring the traffic cameras, roads, smart farming, connected vehicles, air quality, water level, humidity, and carbon dioxide pollution levels in city buildings. One of the major challenges of smart cities is the cyber threat to sensitive data. This paper presents an intelligent approach for detecting cyberattacks and mitigating malicious events in IoT-based smart systems. The proposed approach, known as FCM-SWA, hybridizes a fuzzy C-mean (FCM) with a sperm whale algorithm (SWA). In the first step, we use a novel SWA optimization algorithm to enhance the FCM performance and provide effective defenses against various types of smart city attacks. Next, we propose an adaptive threshold strategy to enhance the global search capability of SWA and prevent the algorithm from settling into local optima. Lastly, we present an efficient scaling approach that solves the clustering problem and finds the optimal cluster center, striking a balance between exploration and exploration in the search space. The proposed FCM-SWA model does better than related and state-of-the-art methods in terms of accuracy, detection rate, precision rate, and F1-scores, as shown by experiments on the NSL-KDD, AWID, and BoT-IoT datasets.

Nano-chitosan-coated, green-synthesized selenium nanoparticles as a novel antifungal agent against Sclerotinia sclerotiorum: in vitro study

AbstractChemical fungicides have been used to control fungal diseases like Sclerotinia sclerotiorum. These fungicides must be restricted because of their toxicity and the development of resistance strains. Therefore, utilizing natural nanoscale materials in agricultural production is a potential alternative. This work aimed to investigate the antifungal properties of a nanocomposite (nano-chitosan-coated, green-synthesized selenium nanoparticles) against the plant pathogenic fungus S. sclerotiorum. Chemical reduction was used to produce selenium nanoparticles from citrus peel extracts, and ionotropic gelation was used to produce chitosan nanoparticles. The nanocomposite has been produced using selenium nanoparticles stabilized by chitosan and cross-linked with sodium tripolyphosphate. Transmission electron microscopy, dynamic light scattering, X-ray diffraction, UV-VIS spectroscopy, and Fourier transform infrared spectroscopy were used to characterize all produced nanostructures. The in vitro antifungal activity and minimum inhibitory concentration of all bulk and nanostructures are investigated at (0.5, 1, 5, 10, 50, 100) ppm concentrations. Scanning electron microscopy was used to detect structural deformations in the fungal mycelium. The findings support the successful synthesis and characterization of all nanoparticles. Lemon peel extract produced smaller, more stable, and distributed selenium nanoparticles (42.28 ± 18.5 nm) than orange peel extract (85.7 ± 140.22 nm). Nanostructures, particularly nanocomposite, have shown a considerable increase in antifungal efficacy compared to bulk structures. At a minimum inhibitory concentration of 0.5 ppm, the nanocomposite exhibited 100% inhibitory activity. The nanocomposite with a concentration of 0.5 ppm exhibited the lowest average fungal biomass (0.32 ± 0.05 g) among all tested nanostructures. Fungal hyphae treated with 0.5 ppm of nanocomposite within 18 h of treatment revealed substantial damage and deformation. These results provide new insights into the nanocomposite as an eco-friendly and promising antifungal agent against other plant pathogenic fungi.

Electro-polymerization of modified carbon paste sensor for detecting azithromycin

Abstract Azithromycin (AM) is one of the prescribed drugs in pandemic medication treatment which has paid great attention. We developed in this study a simply modified carbon paste electrode (CPE) to detect AM using poly-threonine (PT). PT or similar polymers are used as carriers to enhance the delivery and effectiveness of AM. The work was characterised via Cyclic Voltammetry (CV), electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM). We take into consideration the effects of pH, scan rate, accumulation time, interference, and calibration curve.A very sensitive response to the oxidation of 1.0 mM from AM in phosphate buffer solution (PBS) over a pH range of 5.0 to 10.0 was observed using the developed poly-threonine carbon paste electrode (PTCPE). The impact of different AM concentrations was investigated resulting in a detection limit of 0.32 µM and a quantification limit of 1.07 µM at PBS (pH 7.4). Finally, the recently used electrode realized acceptable sensitivity and consistency for AM detection in pharmaceutical drugs.

A novel RFE-GRU model for diabetes classification using PIMA Indian dataset

AbstractDiabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal failure, and heart attack. Diabetes requires the most machine learning help to diagnose diabetes illness at an early stage, as it cannot be treated and adds significant complications to our health-care system. The diabetes PIMA Indian dataset (PIDD) was used for classification in several studies, it includes 768 instances and 9 features; eight of the features are the predictors, and one feature is the target. Firstly, we performed the preprocessing stage that includes mean imputation and data normalization. Afterwards, we trained the extracted features using various types of Machine Learning (ML); Random Forest (RF), Logistic Regression (LR), K-Nearest neighbor (KNN), Naïve Bayes (NB), Histogram Gradient Boost (HGB), and Gated Recurrent Unit (GRU) models. To achieve the classification for the PIDD, a new model called Recursive Feature Elimination-GRU (RFE-GRU) is proposed in this paper. RFE is vital for selecting features in the training dataset that are most important in predicting the target variable. While the GRU handles the challenge of vanishing and inflating gradient of the features results from RFE. Several predictive evaluation metrics, including precision, recall, F1-score, accuracy, and Area Under the Curve (AUC) achieved 90.50%, 90.70%, 90.50%, 90.70%, 0.9278, respectively, to verify and validate the execution of the RFE-GRU model. The comparative results showed that the RFE-GRU model is better than other classification models.

Untargeted metabolomics reveal the corrective effects of scorpion on epileptic mice

Characterisation of precursory seismic activity towards early warning of landslides via semi-supervised learning

AbstractThis study demonstrates that machine learning from seismograms, obtained from commonly deployed seismometers, can identify the early stages of slope failure in the field. Landslide hazards negatively impact the economy and public through disruption, damage of infrastructure and even loss of life. Triggering factors leading to landslides are broadly understood, typically associated with rainfall, geological conditions and steep topography. However, early warning at slope scale of an imminent landslide is more challenging. Through semi-supervised learning for seismic event detection from continuous seismic recordings over a period of about 10 years, we demonstrate that timely landslide induced displacement prediction is possible, providing the basis for landslide early warning systems. Our proposed methodology detects and characterises seismic precursors to landslide events making use of seismic recordings near an active slow moving earth slide-flow using a semi-supervised Siamese network. This data driven methodology identifies increase in microseismicity, and the change in the frequency spectrum of that microseismicity which identify key stages prior to a failure: ‘rest’, ‘precursor’ and ‘active’. Due to the semi-supervised nature of Siamese networks, the methodology is adaptable to discovering new types of distinct events, making it an ideal solution for precursor detection at new sites.

Carboxymethyl cellulose/shellac composite loaded with pomegranate extract and jojoba oil as anti-mycotic and anti-mycotoxigenic food packaging materials

AbstractFood commodities, including mycotoxins naturally produced from toxigenic fungi (pre- or post-harvest), are particularly vulnerable to contamination. The study intended to use unique bioactive composites loaded with antimicrobial constituents for food packaging. Three composite types are based on carboxymethyl cellulose/shellac (CMC/SH) and loaded with pomegranate extract (POE) with or without jojoba oil (JOE) at various concentrations. An enhancement was recorded for tensile strength and elongation at break and burst properties of the composites, where the results point out the amelioration of flexibility and elasticity with E9 (0.3/3 mg/mL of POE/JO). Moreover, E10 (0.3/1 of POE/JOE) content had higher phenolic and flavonoids, with significant antioxidants and the best antimicrobial and anti-mycotoxigenic activity. Six higher antimicrobial composites were chosen for corn seed coating applications in a simulated experiment of toxigenic fungal contamination, where the results recommend E10 as the best formula for packaging application. The E10 was characterized for emulsion stability, particle size, zeta potential, pH, PDI, and acidity that were recorded at 88.16 ± 2.87%, 54.81 nm, 38.74 mV, 6.34 ± 0.54, 31.12 ± 1.02, and 6.02 ± 0.34 mg/L, respectively. The in-silico study revealed that ellagic acid and hesperidin in POE extract, erucic and oleic acids in JOE, and shellac had the highest binding free energies against the vital enzymes involved in bactericidal/bacteriostatic effects and the aflatoxin bio synthetic mechanism.