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Abstract With the ability of servers to remotely control and manage a mobile robot, mobile robots are becoming more widespread as a form of remote communication and human-robot interaction. Controlling these robots, however, can be challenging because of their power consumption, delays, or the challenge of selecting the right robot for a certain task. This paper introduces a novel methodology for enhancing the efficacy of a mobile robotic network. The key two contributions of our suggested methodology are: I: A recommended strategy that eliminates the unwieldy robots before selecting the ideal robot to satisfy the task. II: A suggested procedure that uses a fuzzy algorithm to schedule the robots that need to be recharged. Since multiple robots may need to be recharged at once, this process aims to manage and control the recharging of robots in order to avoid conflicts or crowding. The suggested approach aims to preserve the charging capacity, physical resources (e.g. Hardware components), and battery life of the robots by loading the application onto a remote server node instead of individual robots. Furthermore, our solution makes use of fog servers to speed up data transfers between smart devices and the cloud, it is also used to move processing from remote cloud servers closer to the robots, improving on-site access to location-based services and real-time interaction. Simulation results showed that, our method achieved a 2.4% improvement in average accuracy and a 2.2% enhancement in average power usage over the most recent methods in the same comparable settings.

Abstract In this study, bupivacaine (BUP) and meloxicam (MLX) were simultaneously assayed in their co-formulated ampoules without interference using four affordable, sensitive, and eco-friendly spectrophotometric methods. The assay of MLX at 359.3 nm over the concentration range of 1.0–15.0 µg/mL was accomplished using a direct UV-spectrophotometric method (Method I) without interference from BUP. However, there was a significant overlap between the spectra of BUP and MLX, making it difficult to determine BUP directly from the UV spectrum. Therefore, various UV-based techniques, including second derivative spectrophotometry (Method II), ratio subtraction method (Method III), and absorption factor method (Method IV), were used to determine BUP over the concentration range of 5.0–80.0 µg/mL. The proposed methods could simultaneously determine the studied drugs with a challenging ratio of 33.3:1.0 (BUP: MLX), which increases the importance of the current study. The proposed methods were applied to estimate the studied drugs in commercial ampoules with high % recoveries and low %RSD values. The excellent eco-friendliness of the developed methods was demonstrated using GAPI and AGREE metrics. The developed methods were validated according to ICHQ2(R2) guidelines. The proposed methods can be better suited for the routine analysis of BUP and MLX in their fixed-dose combination with high selectivity.

Abstract This scientific paper presents a novel approach to explore and predict the potential of imidazole-based organic dyes for use in Dye-Sensitized Solar Cells (DSSCs) using a machine learning web application. The design of efficient and cost-effective organic dyes is critical to enhance the performance of DSSCs. Traditional experimental methods are time-consuming and resource-intensive, making it challenging to screen a large number of potential dyes. In this study, we propose a machine learning-based approach to accelerate the discovery process by predicting the photovoltaic performance of imidazole-based organic dyes. Machin learning predictions provide valuable insights into the expected PCE% and behaviors of the molecules toward DSSCs. Based on the RDKit library, several fingerprints such as Molecular ACCess System, Avalon, Daylight, Pharmacophore and Morgan with different radius (r2, r3, r4), were studied. In addition, more than 20 ML algorithms using different cross validation (3, 5, 7, 10) were also evaluated. Among of these, Deep Neural Network models of MLPRegressor algorithm based on the daylight fingerprint shows a significant coefficient of determination combined with the lowest errors. Utilize the trained ML models to screen of 50 million SMILE structure for identify promising imidazole and nitrogen-containing derivative as a doner group. By replacing the donor groups in the well-known MK2 dye structure with the top imidazole derivatives proposed by machine learning, significant improvements in PCE were observed, increasing from 7.70% to as high as 11.49%, representing nearly a 50% enhancement over the control. DFT calculations confirm the ML predictions and clarify the significantly higher oscillator strength and charge transfer properties of MK2-DM1, compared to MK2. This result provides a promising pathway for developing new dye materials that can push the efficiency limits of DSSCs, leading to more efficient solar energy conversion technologies in the future. In addition, a developed web application offers a user-friendly interface for researchers to input their molecular structures and obtain PCE% predictions toward DSSCs. This information can guide researchers in designing a new imidazole dye with high photovoltaic performance to validate and refine the predictions without time consuming.

Abstract We characterize luminosity components of Ultra/Luminous Infrared Galaxies (U/LIRGs) in multi-wavelength from the X-ray to far-infrared. A set of 63 AGN U/LIRGs was selected where these galaxies are powered by a central active galactic nucleus (AGN). Utilizing the X-CIGALE code, SEDs for these galaxies are carried out where their SEDs are fitted with observations. Accordingly, the physical parameters such as the stellar mass, the dust-to-gas mass ratio, and the star formation rate are calculated. The total luminosity and its decomposed components (stellar, AGN, X-ray) are also calculated. We characterized these luminosities in relative to the intrinsic luminosity and in relative to each other. As a function of the stellar mass, these luminosities reveal an increase with different correlation coefficients, showing a strong correlation. In correlation with the intrinsic AGN power, the stellar, AGN, and X-ray luminosities are strongly correlates in their variation to the intrinsic AGN luminosity, showing stronger correlations of AGN, and X-ray luminosities than those of the stellar one. In relationships between various luminosity components, both the stellar and X-ray luminosities reveal strong correlations with the AGN luminosity. On the other hand, the X-ray luminosity varies strongly with the stellar luminosity and moderately with IR luminosity. Compared to obscured AGN galaxies, both the stellar and AGN luminosities similarly vary with increasing the intrinsic power of the active nucleus but for obscured AGN they are faster in their variation than that of U/LIRG. These correlations may offer valuable insights to understand the physical properties and their relationships through the evolution of U/LIRGs.

Abstract Floods accompanied by thunderstorms in developed cities are hazardous, causing damage to infrastructure. To secure infrastructure, it is important to employ an integrated approach, combining remote sensing, GIS and precipitation data. The model was developed based on the estimation of event-based runoff and investigated the relationship between runoff and impervious surfaces. The novel approach of combining Hydrologic Engineering Center’s River Analysis System (HEC-GeoRAS) along with satellite imagery was utilized, where spatial data was combined with real-time values to run the model. As a first step, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was fed with information about precipitation, slope, soil type, as well as land use and land cover. The results reveal that the subbasins of Deira, Nief and Jumeirah have the largest impervious area and, thus, a higher probability of flood occurrence. The model was calibrated and validated using previous runoff events and by comparing observed and simulated streak flow and peak discharge against those reported in previous studies. It was found that the model is efficient and can be used in similar regions.

Abstract The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.

Abstract A mathematical analysis was conducted to predict the power requirements of the olive trunk shaker. The predicted required power of the olive trunk shaker is influenced by shaking displacement, fruit detachment force, fruit mass, stem length, damping ratio, unbalanced mass of the tree shaker, and exciting shaking frequency. The Olive trunk shaker was tested in the field at five attachment heights, 400, 500, 600, 700, and 800 mm, and six levels of olive trunk diameter: 78, 84, 101, 143, 158, and 186 mm. The results indicated that the theoretical shaking power was 12.8 kW while the actual required shaking power ranged from 7.1 to 17.8 kW, with an average of 12.6 kW due to variations in tree trunk diameters and different attachment heights. Multiple regression using the least squares method was employed to establish a statistical relation between the theoretical equation and the actual required power as a function of the clamp attachment height and trunk diameter. The regression coefficient of the predicted equation was 93.6% for attachment height values between 400 and 800 mm and tree trunk diameter values between 78 and 186 mm, with a mean absolute error of the prediction model was 0.5009 kW.

Abstract Polylactic acid modified with graphene oxide (PLA/GO) is proposed to interact with ZnO through 6 different schemes. Density functional theory at B3LYP/LANL2DZ level was utilized to calculate total dipole moment (TDM), HOMO/LUMO energy gap (ΔE) and to map the molecular electrostatic potential (MESP). Results indicated that PLA/GO interacted with ZnO through O-atom forming PLA/GO/OZn composite. This composite interacts with methane, hydrogen sulfide, humidity (H2O), carbon dioxide and ethanol. The same gases were supposed to interact further with PLA/GO/Cu2O. Adsorption energy for the interaction between each composite and the proposed gases were calculated. Both PLA/GO/OZn and PLA/GO/Cu2O composites interacted favorably with H2O. Adsorption energy for interaction of other gases with studied structures are generally low compared to H2O. PLA/GO/OZn have adsorption energy slightly higher than that of PLA/GO/Cu2O. PLA/GO/OZn has higher TDM values than those of PLA/GO/Cu2O, indicating a more polar material. Conversely, PLA/GO/Cu2O exhibited larger ΔE values than those of PLA/GO/OZn. TDM and energy gap results for both studied structures indicated good sensing capabilities. Further insights come from analyzing the calculated density of states (DOS) and partial density of states (PDOS). PLA/GO/Cu2O exhibited high peak for copper in its DOS and PDOS spectra compared to zinc and oxygen in case of PLA/GO/OZn. This means a higher density of available electronic states associated with Cu.

Abstract Ornithonyssus sylviarum (O. sylviarum) is an obligatory, blood-sucking ectoparasite widely distributed among poultry and other mammals, causing significant economic losses. This study represented the first report of molecular genotypic identification of O. sylviarum from pigeons, Columba livia domestica, in Egypt. PCR and sequencing of the 28S rRNA gene were conducted. The resulting mite sequences were subjected to BLAST analysis, revealing 90–100% similarity to O. sylviarum in all tested samples. The sequences were deposited in GenBank under the accession numbers PP049086 and PP033720. A phylogenetic tree was constructed to compare the obtained species with related species worldwide. Additionally, infected pigeons showed increased expression of IL-1, IL-10, IFN-γ, and TGF-β3 genes and elevated serum levels of stress biomarkers. The increased level of these cytokines indicates there was a disturbance in the immune status of the infected host with parasite compared with control healthy ones. This increases the susceptibility to infection with other pathogens.