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Colorectal cancer (CRC) is the fourth most common cause of malignant tumor death. The development of novel, more effective drugs is desperately needed to treat CRC. Zingiber officinale is believed to possess anticancer properties due to its flavonoids and phenols. Using Soxhlet (SOXT) and maceration (MACR) techniques, the present study aimed to evaluate the amounts of quercetin, gallic acid, rutin, naringin, and caffeic acid in ginger capsules of Z. officinale. High‐performance liquid chromatography (HPLC)/ultraviolet was used for separation and quantitation. In vitro toxicity evaluation of ginger capsules on the CRC cell line HT‐29 was also conducted to assess the anticancer activity of the supplement. The cell line HT‐29 (HTB‐38) colorectal adenocarcinoma was utilized for the antiproliferative effect of 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyl tetrazolium bromide. Ginger herbal supplement extract at dosages of 200 and 100 μg had strong cytotoxic effects (IC50 < 50 μg/mL) on HT‐29 CRC cells via MACR. This extract is comparable to the SOXT extract, which has an IC50 of less than 50 μg/mL. The anticancer effect of ginger herbal supplement formulations against CRC lines was investigated, and the results obtained from both the MACR and SOXT extraction procedures were noteworthy. The quercetin content was the highest of all the extracts according to the HPLC data. [ABSTRACT FROM AUTHOR]

In this study, the polyethylene oxide (PEO)/SiO2 nanoparticles (NPs) nanocomposite films with various SiO2 NPs concentrations were prepared using an in situ formation of NPs in the polymer matrix for self‐cleaning antireflected surface applications. The effect of SiO2 NPs in PEO/SiO2 NPs nanocomposite films on the structural, morphological, chemical, thermal, optical, and electrical properties of PEO/SiO2 NPs nanocomposite films was performed. According to the x‐ray diffraction and the differential scanning calorimetry analysis, the crystallinity degree of the nanocomposite films decreases by increasing the SiO2 NPs concentrations. The bandgap energy of PEO/SiO2 NPs nanocomposite films decreases from 3.95 to 3.55 eV as the SiO2 NPs concentration increases up to 10 wt.%. The average electrical conductivity of the PEO/SiO2 NPs nanocomposite films increases from 5.1 × 10−7 to 2.0 × 10−6 S/cm as the SiO2 NPs concentration increases up to 10 wt.%. The refractive index decreases to 1.64 at 550 nm for the PEO/SiO2 NPs nanocomposite films with 10 wt.% of SiO2 NPs, and the water contact angle decreases to around 0° after thermal treatment, which confirms that the PEO/SiO2 NPs nanocomposite films can be used as self‐cleaning antireflected surfaces. [ABSTRACT FROM AUTHOR]

Objectives: To quantify quercetin, gallic acid, rutin, naringin, and caffeic acid in the rhizome of Zingiber officinale different extracts in seven different solvents (methanol, ethanol, ethyl acetate, water, dichloromethane, chloroform, and n-hexane), for the first time, using HPLC/UV. Also, to study the anticancer activity of Zingiber officinale different extracts by evaluating its in vitro toxicity on HT-29 colorectal cancer cell line. Methods: The fresh and dried rhizomes were extracted using Soxhlet (SOX) and maceration (MAC) methods. Separation of compounds was conducted using HPLC. The cell line used for MTT cell proliferation assay antiproliferative; is HT-29 (HTB-38) colorectal adenocarcinoma. Results: The MTT test indicated that powder ginger extracted by MAC or SOX showed high cytotoxicity activity (IC50<50) against HT-29 cells, except water using SOX, which showed mild cytotoxicity activity. The fresh ginger extracted by MAC using dichloromethane and those extracted by SOX using ethyl acetate showed strong cytotoxicity activity (IC50 <50). Conclusion: The phenolic and flavonoid contents of ginger can vary depending on the different extracts from ginger plant. Also, HPLC results revealed that quercetin was the highest in all extracts. [ABSTRACT FROM AUTHOR]

The crystal structure, morphology, and linear and nonlinear optical properties of Co-doped Ba1−xCoxTiO3 (x = 0.00, 0.02, 0.03, 0.04, and 0.06) were investigated using x-ray diffraction (XRD), scanning electron microscopy, and UV–visible spectroscopy measurements. The XRD patterns confirmed that the Ba1−xCoxTiO3 samples crystallized in the perovskite phase, and a transition from quadratic to pseudo-cubic phase was observed with Co doping. Upon doping, the band gap (Eg) decreased from 3.14 eV to 2.11 eV, indicating that Co doping into BaTiO3 caused a change in their electronic levels associated with lattice defects. Additionally, the optical parameters including linear refractive index, extinction coefficient, dielectric coefficient, optical conductivity (σopt), and the static index (n0) were extracted and linked to the Co content. Finally, analysis of the linear (LOp) and nonlinear optical (NLOp) parameters of Ba1−xCoxTiO3 revealed that the increase in Co content induces an increase in the NLOp refractive index (n2) and third-order NLOp susceptibility χ(3). [ABSTRACT FROM AUTHOR]

This study aimed to investigate red-light violations and their influencing factors at traffic signals in four major cities in Jordan. Field data was collected from 30 traffic signals in Amman, Zarqa, Jerash and Irbid cities. The intersection type, leg grade and speed of the subject's approach data was collected through field surveys and measurements, while the red-light violation data was collected from the recorded videos. The study analysis showed that among 3,444 drivers who had the chance to violate a red light, 436 (12.66%) drivers ran a red light. Pickup drivers had the highest violation rate among all drivers, with a rate of 21.4%, while heavy-vehicle drivers showed the lowest percentage of violation, at 11.18% for buses and 8.24% for trucks. The analysis showed that four-leg intersections had a higher violation rate than three-leg intersections. Also, drivers tended to show more violations on upgrade intersection approaches, with almost the same violation behavior on level and downgrade intersection approaches. According to traffic movement type, the analysis results showed that through-moving vehicles had the highest violation rate among all movement types, followed by left-turners, while the lowest violation rate was observed among U-turn movements. Referring to the location of the intersection, the violation rate was 13.48% in urban areas and 11.37% in sub-urban areas. The results showed that drivers tend to violate red lights more during fall than in spring. It was found that about 21% of drivers who arrived at the intersection at the beginning of red time violated the red light, while only 3.53% violated the red light during the rest of the red time interval. In addition, the percentage of red-light violations was directly proportional to the subject approach speed and total traffic volume entering the intersection and was inversely proportional to the conflicting traffic volumes. [ABSTRACT FROM AUTHOR]

Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML)models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication. In this aspect, this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder (XAIIDS-FSDVAE) model for CPS. The proposed model encompasses the design of coyote optimization algorithm (COA) based feature selection (FS) model is derived to select an optimal subset of features. Next, an intelligent Dirichlet Variational Autoencoder (DVAE) technique is employed for the anomaly detection process in the CPS environment. Finally, the parameter optimization of the DVAEtakes place using amanta ray foraging optimization (MRFO) model to tune the parameter of the DVAE. In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique, a wide range of simulations take place using the benchmark datasets. The experimental results reported the better performance of the XAIIDS-FSDVAE technique over the recent methods in terms of several evaluation parameters. [ABSTRACT FROM AUTHOR]

We present a case of incidentally discovered gas bubbles flowing within the inferior vena cava during a routine abdominal sonographic examination, that subsequently unmasked a previously undiagnosed emphysematous cystitis. [ABSTRACT FROM AUTHOR]

In this study, experimental and numerical simulations were conducted to examine Acid orange 7 (AO7) adsorption by sodium hydroxide treated eggshells (SHTES). The tests were carried out in a continuous fixed-bed column of 10 cm. The Effects of initial dye concentration (30, 50 and 80 mg/L) and flow rate (2, 4 and 6 ml/min) on the column performance were also investigated. A two-dimensional (2D) axisymmetric mathematical model based on conservation equations of movement and species was used to predict the breakthrough curves of the fixed-bed for AO7 adsorption. The obtained experimental results were used to validate this model; a good compatibility has been noted between the numerical and experimental results of the breakthrough curves. [ABSTRACT FROM AUTHOR]

Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661. [ABSTRACT FROM AUTHOR]