Explore the vast range of titles available.

Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency.

This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance. The LGB model achieved the best results, with an R 2 of 0.988 on the test set and low error rates (RMSE of 9.1284E-05 and MAE of 5.85322E-05), surpassing other models in predictive accuracy and generalizability. Parity plots confirmed the LGB model’s close alignment between predicted and actual solubility values, highlighting its robust performance. Furthermore, 3D surface plots and partial effect plots demonstrated LGB’s capacity to model solubility across different solvent systems, capturing complex interactions between T, w, and solvent effects. Finally, the LGB model predicted maximum solubility at a temperature of 305.76 K and a mass fraction of 0.753 in a dichloromethane + methanol mixture, providing valuable insights for solubility optimization in solvent selection. This work underscores the effectiveness of the LGB model for solubility prediction, with potential applications in formulation and experimental planning.

Analysis of small-molecule drug solubility in binary solvents at different temperatures was carried out via several machine learning models and integration of models to optimize. We investigated the solubility of rivaroxaban in both dichloromethane and a variety of primary alcohols at various temperatures and concentrations of solvents to understand its behavior in mixed solvents. Given the complex, non-linear patterns in solubility behavior, three advanced regression approaches were utilized: Polynomial Curve Fitting, a Bayesian-based Neural Network (BNN), and the Neural Oblivious Decision Ensemble (NODE) method. To optimize model performance, hyperparameters were fine-tuned using the Stochastic Fractal Search (SFS) algorithm. Among the tested models, BNN obtained the best precision for fitting, with a test R² of 0.9926 and a MSE of 3.07 × 10⁻⁸, proving outstanding accuracy in fitting the rivaroxaban data. The NODE model followed BNN, showing a test R² of 0.9413 and the lowest MAPE of 0.1835. The Polynomial model yielded a lower test R² of 0.8200 and higher error rates, indicating its limitations in unravelling the underlying relationships for the solubility variations. This study shows that advanced machine learning models, particularly BNN and NODE, can predict pharmaceutical solubility and improve crystallization process design and optimization.

Molecular diffusion of drugs is of major importance for development and understanding drug delivery systems. Indeed, the main phenomenon which is controlling the rate of release is molecular diffusion which can be controlled via different phenomena such as interactions with the drug carrier and solution. In this work, we developed a novel hybrid model based on mass transfer and machine learning for predicting drug diffusion in a 3D space. The mass transfer equation including diffusion is solved in the domain and then the data is extracted for building machine learning models. The present study presents the findings of an analysis conducted with the objective of constructing precise regression models for the prediction of chemical species concentration ( C ) for a drug diffusion through a three-dimensional space, utilizing coordinates (x, y, z). The dataset comprises over 22,000 data points, with each point containing the coordinates ( ) and the corresponding concentration ( C ) in mol/m³. We employ three tree-based ensemble models: Kernel Ridge Regression (KRR), -Support Vector Regression ( -SVR), and Multi Linear Regression (MLR) for modeling the relationship between spatial coordinates and the concentration. Hyperparameter optimization is performed using the Bacterial Foraging Optimization Algorithm (BFO) to fine-tune the models. The results reveal that -SVR has the highest performance with a score of 0.99777 in terms of R 2 , followed by KRR with an R 2 score of 0.94296, and MLR with an R 2 value of 0.71692. Additionally, -SVR exhibits the lowest RMSE and MAE, showing excellent predictive accuracy compared to KRR and MLR. Overall, our analysis demonstrates the effectiveness of employing tree-based ensemble models coupled with BFO for accurately predicting chemical concentrations in three-dimensional space, with -SVR emerging as the most promising model for this task. These findings have implications for various applications such as environmental monitoring, pollutant dispersion modeling, and chemical process optimization.

Mental disorders have a poor clinical prognosis and account for approximately 8% of the global burden of disease. Some examples of mental disorders are anxiety and depression. Conventional antidepressants have limited efficacy in patients because their pharmacological effects wear off, and side effects increase with prolonged use. It is claimed that herbal medicine’s antioxidant capacity helps regulate people’s mood and provide a more substantial pharmacological effect. With this background, the purpose of this study is to investigate the effect of rutin on reserpine-induced anxiety and depression in rats. The animals were divided into groups of six rats each: normal control (water), a depression model, a rutin-treated rat model, and an amitriptyline-treated rat model. According to the results, 14 days of treatment with rutin, once daily, showed a modest antidepressant effect. This effect was mediated by increased serotonin, norepinephrine, and dopamine levels in cortical and hippocampal regions. The antioxidant and vasodilator properties of rutin may contribute to its antidepressant properties. According to this study, rutin has shown antidepressant effects by reducing antioxidant activity and acetylcholinesterase.

The role that vehicular fog computing based on the Fifth Generation (5G) can play in improving traffic management and motorist safety is growing quickly. The use of wireless technology within a vehicle raises issues of confidentiality and safety. Such concerns are optimal targets for conditional privacy-preserving authentication (CPPA) methods. However, current CPPA-based systems face a challenge when subjected to attacks from quantum computers. Because of the need for security and anti-piracy features in fog computing when using a 5G-enabled vehicle system, the L-CPPA scheme is proposed in this article. Using a fog server, secret keys are generated and transmitted to each registered car via a 5G-Base Station (5G-BS) in the proposed L-CPPA system. In the proposed L-CPPA method, the trusted authority, rather than the vehicle’s Onboard Unit (OBU), stores the vehicle’s master secret data to each fog server. Finally, the computation cost of the suggested L-CPPA system regards message signing, single verification and batch verification is 694.161 ms, 60.118 ms, and 1348.218 ms, respectively. Meanwhile, the communication cost is 7757 bytes.

In this study, elastic nanovesicles, constructed of phospholipids optimized by Quality by Design (QbD), release 6-gingerol (6-G), a natural chemical that may alleviate osteoporosis and musculoskeletal-related pain. A 6-gingerol-loaded transfersome (6-GTF) formulation was developed using a thin film and sonication approach. 6-GTFs were optimized using BBD. Vesicle size, PDI, zeta potential, TEM, in vitro drug release, and antioxidant activity were evaluated for the 6-GTF formulation. The optimized 6-GTF formulation had a 160.42 nm vesicle size, a 0.259 PDI, and a −32.12 mV zeta potential. TEM showed sphericity. The 6-GTF formulation’s in vitro drug release was 69.21%, compared to 47.71% for the pure drug suspension. The Higuchi model best described 6-G release from transfersomes, while the Korsmeyer–Peppas model supported non-Fickian diffusion. 6-GTF had more antioxidant activity than the pure 6-G suspension. The optimized transfersome formulation was converted into a gel to improve skin retention and efficacy. The optimized gel had a spreadability of 13.46 ± 4.42 g·cm/s and an extrudability of 15.19 ± 2.01 g/cm2. The suspension gel had a 1.5 μg/cm2/h ex vivo skin penetration flux, while the 6-GTF gel had 2.71 μg/cm2/h. Rhodamine B-loaded TF gel reached deeper skin layers (25 μm) compared to the control solution in the CLSM study. The gel formulation’s pH, drug concentration, and texture were assessed. This study developed QbD-optimized 6-gingerol-loaded transfersomes. 6-GTF gel improved skin absorption, drug release, and antioxidant activity. These results show that the 6-GTF gel formulation has the ability to treat pain-related illnesses effectively. Hence, this study offers a possible topical treatment for conditions connected to pain.

Pseudomonas aeruginosa is an opportunistic bacterium causing several health problems and having many virulence factors like biofilm formation on different surfaces. There is a significant need to develop new antimicrobials due to the spreading resistance to the commonly used antibiotics, partly attributed to biofilm formation. Consequently, this study aimed to investigate the anti-biofilm and anti-quorum sensing activities of Dioon spinulosum, Dyer Ex Eichler extract (DSE), against Pseudomonas aeruginosa clinical isolates. DSE exhibited a reduction in the biofilm formation by P. aeruginosa isolates both in vitro and in vivo rat models. It also resulted in a decrease in cell surface hydrophobicity and exopolysaccharide quantity of P. aeruginosa isolates. Both bright field and scanning electron microscopes provided evidence for the inhibiting ability of DSE on biofilm formation. Moreover, it reduced violacein production by Chromobacterium violaceum (ATCC 12,472). It decreased the relative expression of 4 quorum sensing genes ( las I, las R, rhl I, rhl R) and the biofilm gene ( ndv B) using qRT-PCR. Furthermore, DSE presented a cytotoxic activity with IC 50 of 4.36 ± 0.52 µg/ml against human skin fibroblast cell lines. For the first time, this study reports that DSE is a promising resource of anti-biofilm and anti-quorum sensing agents.

Methyl anthranilate (MA) is a naturally derived compound commonly used in cosmetic products, such as skin care products, fine perfumes, etc. The goal of this research was to develop a UV-protective sunscreen gel using methyl-anthranilate-loaded silver nanoparticles (MA-AgNPs). The microwave approach was used to develop the MA-AgNPs, which were then optimized using Box–Behnken Design (BBD). Particle size (Y1) and absorbance (Y2) were chosen as the response variables, while AgNO3 (X1), methyl anthranilate concentration (X2), and microwave power (X3) were chosen as the independent variables. Additionally, the prepared AgNPs were approximated for investigations on in vitro active ingredient release, dermatokinetics, and confocal laser scanning microscopy (CLSM). The study’s findings showed that the optimal MA-loaded AgNPs formulation had a particle size, polydispersity index, zeta potential, and percentage entrapment efficiency (EE) of 200 nm, 0.296 mV, −25.34 mV, and 87.88%, respectively. The image from transmission electron microscopy (TEM) demonstrated the spherical shape of the nanoparticles. According to an in vitro investigation on active ingredient release, MA-AgNPs and MA suspension released the active ingredient at rates of 81.83% and 41.62%, respectively. The developed MA-AgNPs formulation was converted into a gel by using Carbopol 934 as a gelling agent. The spreadability and extrudability of MA-AgNPs gel were found to be 16.20 and 15.190, respectively, demonstrating that the gel may spread very easily across the skin’s surface. The MA-AgNPs formulation demonstrated improved antioxidant activity in comparison to pure MA. The MA-AgNPs sunscreen gel formulation displayed non-Newtonian pseudoplastic behaviour, which is typical of skin-care products, and was found to be stable during the stability studies. The sun protection factor (SPF) value of MA-AgNPG was found to be 35.75. In contrast to the hydroalcoholic Rhodamine B solution (5.0 µm), the CLSM of rat skin treated with the Rhodamine B-loaded AgNPs formulation showed a deeper penetration of 35.0 µm, indicating the AgNPs formulation was able to pass the barrier and reach the skin’s deeper layers for more efficient delivery of the active ingredient. This can help with skin conditions where deeper penetration is necessary for efficacy. Overall, the results indicated that the BBD-optimized MA-AgNPs provided some of the most important benefits over conventional MA formulations for the topical delivery of methyl anthranilate.