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The neuro-stimulant anti-narcoleptic drug as modafinil (MOD) is used to treatment neurological conditions caused by COVID-19. MOD was used to treatment narcolepsy, shift-work sleep disorder, and obstructive sleep apnea-related sleepiness. So, an innovative, quick, economical, selective, and ecologically friendly procedure was carried out. A highly sensitive N@CQDs technique was created from green Eruca sativa leaves in about 4 min using microwave synthesis at 700 w. The quantum yield of the synthesized N@CQDs was found to be 41.39%. By increasing the concentration of MOD, the quantum dots' fluorescence intensity was gradually quenched. After being excited at 445 nm, the fluorescence reading was recorded at 515 nm. The linear range was found to be in the range 50 – 700 ng mL −1 with lower limit of quantitation (LOQ) equal to 45.00 ng mL −1 . The current method was fully validated and bio analytically according to (US-FDA and ICH) guidelines. Full characterization of the N@CQDs has been conducted by high resolution transmission electron microscope (HRTEM), Zeta potential measurement, fluorescence, UV–VIS, and FTIR spectroscopy. Various experimental variables including pH, QDs concentration and the reaction time were optimized. The proposed study is simply implemented for the therapeutic drug monitoring system (TDMS) and various clinical laboratories for further pharmacokinetic research.

Carbon dots doped with copper(II) and nitrogen (Cu,N@C-dots) were prepared and are shown to be viable fluorescent nanoprobe for pyrogallol (PGL) was developed for the first time. The reaction is based on (a) the complexation reaction between Cu,N@C-dots and catechol moiety, and (b) the generation of a quinone-like structure. Thus, the co-ordination complex formed between Cu(II) in C-dots and PGL results in quenching of the fluorescence of C-dots. In addition, the formation of a yellow color due to complex formation between the nanoprobe and Cu(II) allowed the colorimetric determination of PGL. The nanoprobe was prepared by thermal synthesis, using ethylenediaminetetraacetic acid salt and copper(II) chloride as sources for carbon, nitrogen and copper, respectively. The carbon dots were characterized by UV-VIS spectroscopy, Fourier transform infrared spectroscopy, powder X-ray diffraction, transmission electron microscopy) and dynamic light scattering. Fluorescence drops linearly in the 0.15 to 70 μM PGL concentration range with a detection limit of 39 nM and a relative standard deviation of 1.8%. The optimal excitation and emission wavelengths are 370 nm and 428 nm, respectively. The colorimetric assay has a linear response at 325 nm absorption wavelengths in the 6 to 140 μM PGL concentration range with a detection limit of 1.8 μM and a 2.3% relative standard deviation. Graphical abstract Dual mode colorimetric and fluorimetric nanoprobe was designated for pyrolgallol determination based on complexation with copper(II)- and nitrogen-doped carbon dots.

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC.

Copper (Cu) plays a role in maintaining healthy nerve cells and the immune system. Osteoporosis is a high-risk factor for Cu deficiency. In the proposed research, unique green, fluorescent cysteine-doped MnO2 quantum dots (Cys@MnO2 QDs) were synthesized and assessed for the determination of Cu in different food and hair samples. The developed quantum dots were synthesized with the help of cysteine using a straightforward ultrasonic approach to create 3D fluorescent Cys@MnO2 QDs. The resulting QDs’ morphological and optical characteristics were carefully characterized. By adding Cu ions, the intensity of fluorescence for the produced Cys@MnO2 QDs was found to be dramatically reduced. Additionally, the applicability of Cys@MnO2 QDs as a new luminous nanoprobe was found to be strengthened by the quenching effect grounded on the Cu–S bonding. The concentrations of Cu2+ ions were estimated within the range of 0.06 to 7.00 µg mL−1, with limit of quantitation equal to 33.33 ng mL−1 and detection limit equal to 10.97 ng mL−1. The Cys@MnO2 QD technique was applied successfully for the quantification of Cu in a variety of foods, including chicken meat, turkey, and tinned fish, as well as in human hair samples. The chance that this novel technique could be a useful tool for figuring out the amount of cysteine in bio-samples is increased by the sensing system’s remarkable advantages, which include being rapid, simple, and economical.

Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and automation is to improve traffic safety, negative safety impacts may persist in the mixed-vehicle environment. Speed disparity measures have been shown in the literature to be related to safety performance. Therefore, speed disparity measures are derived from the expected speed distributions of different vehicle technologies and are used as surrogate measures to assess the safety of mixed-vehicle environments and identify the efficacy of prospective countermeasures. This paper builds on speed models in the literature to predict the speed behavior of CVs, AVs, and DVs on horizontal curves on freeways and major arterials. The paper first proposes a methodology to determine speed disparity measures on horizontal curves without any control in terms of speed limit. The impact of speed limit or advisory speed, as a safety countermeasure, is modeled and assessed using different strategies to set the speed limit. The results indicated that the standard deviation of the speeds of all vehicles (σc) in a mixed environment would increase on arterial roads under no control compared to the case of DV-only traffic. This speed disparity can be reduced using an advisory speed as a safety countermeasure to decrease the adverse safety impacts in this environment. Moreover, it was shown that compared to the practice of a constant speed limit based on road classification, the advisory speed is more effective when it is based on the speed behavior of various vehicle types.

The increase in people’s use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people’s fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

Gefitinib (GFT) is a tyrosine kinase inhibitor drug used as a first-line treatment for patients with advanced or metastatic non-small cell lung, colon, and breast cancer. GFT exhibits low solubility and hence low oral bioavailability, which restricts its clinical application. One of the most important trends in overcoming such problems is the use of a vesicular system. Cubosomes are considered one of the most important vesicular systems used to improve solubility and oral bioavailability. In this study, GFT cubosomal nanoparticles (GFT-CNPs) were prepared by the emulsification method. The selected formulation variables were analyzed and optimized by full factorial design and response surface methodology. Drug entrapment efficiency (EE%), transmission electron microscopy, particle size, polydispersity index, in vitro release and its kinetics, and the effect of storage studies were estimated. The chosen GFT-CNPs were subjected to further investigations as gene expression levels of tissue inhibitors of metalloproteinases-1 (TIMP-1) and matrix metalloproteinases-7 (MMP-7), colon biomarkers, and histopathological examination of colon tissues. The prepared GFT-CNPs were semi-cubic in shape, with high EE%, smaller vesicle size, and higher zeta potential values. The in vivo data showed a significant decrease in the serum level of embryonic antigen (CEA), carbohydrate antigen 19-9 (CA 19-9), and gene expression level of TIMP-1 and MMP-7. Histopathological examination showed enhancement in cancer tissue and highly decreased focal infiltration in the lamina propria after treatment with GFT-CNPs.

Elagolix (ELX) is an orally administered non-peptidic GnRH antagonist that has been approved by the Food and Drug Administration in 2018 for the treatment of endometriosis pain. A sensitive and selective method for estimating elagolix (ELX) in human plasma and content uniformity was developed and validated. The spectrofluorimetric technique was used to investigate ELX utilizing boron-doped carbon quantum dots (B@CQDs). After gradually adding ELX, the quantum dots fluorescence was enhanced with LOQ of 1.74 ng mL−1, the calibration curve between ELX and corresponding fluorescence intensity was found over a range of 4–100 ng mL−1. The method was successfully applied in real human plasma with pharmacokinetic study and content uniformity test. The pharmacokinetic parameters as Cmax were found to be 570 ± 5.32 ng. mL−1 after 1 h, t1/2 was found to be 6.50 h, and AUC was found to be 1290 ± 30.33 ng. h. mL−1. B@CQDs were characterized using variety of instruments. The strategy is simple to implement in clinical labs and therapeutic drug monitoring systems.

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts.