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In this paper, a real-time dynamic programming (RTDP) approach was developed for the first time to jointly carry a slung load using two unmanned aerial vehicles (UAVs) with a trajectory optimized for time and energy consumption. The novel strategy applies RTDP algorithm, where the journey was discretized into horizons consisting of distance intervals, and for every distance interval, an optimal policy was obtained using a dynamic programming sweep. The RTDP-based strategy is applied for dual-UAV collaborative payload transportation using coordinated motion where UAVs act as actuators on the payload. The RTDP algorithm provides the optimal velocity decisions for the slung load transportation to either minimize the journey time or the energy consumption. The RTDP approach involves minimizing a cost function which is derived after simplifying the combined model of the dual-UAV-payload system. The cost function derivation was also accommodated to dynamically distribute the load/energy between two multi-rotor platforms during a transportation mission. The cost function is used to calculate transition costs for all stages and velocity decisions. A terminal cost is used at the last distance interval during the first phase of the journey when the velocity at the end of the current horizon is not known. In the second phase, the last stage or edge of the horizon includes the destination, hence final velocity is known which is used to calculate the transition cost of the final stage. Once all transition costs are calculated, the minimum cost is traced back from the final stage to the current stage to find the optimal velocity decision. The developed approach was validated in MATLAB simulation, software in the loop Gazebo simulation, and real experiments. The numerical and Gazebo simulations showed the successful optimization of journey time or energy consumption based on the selection of the factor λ. Both simulation and real experiments results show the effectiveness and the applicability of the proposed approach.

Bioethanol-derived biomass is a green sustainable source of energy that is highly recommended as an efficient alternative to the replacement of fossil fuels. However, this type of bioethanol production is always expensive with very low bioethanol concentration. Therefore, this work aims to represent a facile and green approach for bioethanol production with high concentration and purity as well as reasonable cost from corn stover (CS). The goal of this study is to characterize CS and its treated samples with maleic acid (CSM) using various characterization analyses, such as proximate and ultimate analysis, HHV, TGA, FTIR, SEM, and CHNS. The bioethanol production stages: Pretreatment, enzymatic degradation, fermentation, and finally bioethanol separation and purification via the pervaporation process, which have been investigated and optimized are associated with the economic analysis. The optimum operating condition of the pretreatment process was 2% maleic acid, 1:20 solid-to-liquid ratio at 45 psi, 120 °C, and 1 h of operation in the autoclave. This process contributes to 53 and 45% lignin and hemicellulose removal, 98% cellulose recovery, and a glucose yield of 741 mg/dL. The yeast isolate succeeded in the production of 1230 mg/dL of bioethanol. This isolated yeast strain was close to Pichia nakasei with a similarity of 98%, and its amplified 18S rRNA gene sequence was deposited in GenBank with the accession number MZ675535. Poly (MMA-co-MA) membrane was synthesized, characterized, and its efficiency for increasing the bioethanol concentration was evaluated using the integrated pervaporation technique. The techno-economic analysis is presented in detail to evaluate the process profitability, which achieves a considerable profit for the whole duration of the project without any losses as it reaches a net profit of USD 1 million in 2023, reaching USD 2.1 million in 2047 for a company with a capacity of 32 thousand tons per year. The sequential strategy offers a promising approach for efficient bioethanol production under mild and environmentally friendly conditions that enable its implication industrially.

Lung cancer is a worldwide prevalent malignancy. This disease has a low survival rate due to diagnosis at a late stage challenged by the involvement of metastatic sites. Non-small-cell lung cancer (NSCLC) is presented in 85% of cases. The last decade has experienced substantial advancements in scientific research, leading to a novel targeted therapeutic approach. The newly developed pharmaceutical agents are aimed towards specific mutations, detected in individual patients inflicted by lung cancer. These drugs have longer and improved response rates compared to traditional chemotherapy. Recent studies were able to identify rare mutations found in pulmonary tumors. Among the gene alterations detected were mesenchymal epithelial transition factor (MET), human epidermal growth factor 2 (HER2), B-type Raf kinase (BRAF), c-ROS proto-oncogene (ROS1), rearranged during transfection (RET) and neurotrophic tyrosine kinase (NTRK). Ongoing clinical trials are gaining insight onto possible first and second lines of medical treatment options intended to enable progression-free survival to lung cancer patients.

The wide distribution of infections-related pathogenic microbes is almost related to the contamination of food and/or drinking water. The current applied treatments face some limitations. In the current study, k-carrageenan polymer was used as supporting material for the proper/unreleased silver nanoparticles that showed strong antimicrobial activity against six pathogenic bacteria and yeast. The bio-extract of the pupa of green bottle fly was used as the main agent for the synthesis of silver nanoparticles. The qualitative investigation of biologically synthesized silver nanoparticles was determined using UV-Vis spectrophotometric analysis; however, the size of nanoparticles was in range of 30–100 nm, as confirmed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and particle size analyzer. The proper integration of silver nanoparticles into the polymeric substrate was also characterized through fourier transform infrared (FT-IR), thermogravimetric analysis (TGA), SEM, and tensile strength. The antimicrobial activity of k-carrageenan/silver nanoparticles against Gram positive, Gram negative, and yeast pathogens was highly effective. These results indicate the probable exploitation of the polymeric/nanoparticles composite as an extra stage in water purification systems in homes or even at water treatment plants.

This paper presents a strategy to autonomously explore unknown indoor environments, focusing on 3D mapping of the environment and performing grid level semantic labeling to identify all available objects. Unlike conventional exploration techniques that utilize geometric heuristics and information gain theory on an occupancy grid map, the work presented in this paper considers semantic information, such as the class of objects, in order to gear the exploration towards environmental segmentation and object labeling. The proposed approach utilizes deep learning to map 2D semantically segmented images into 3D semantic point clouds that encapsulate both occupancy and semantic annotations. A next-best-view exploration algorithm is employed to iteratively explore and label all the objects in the environment using a novel utility function that balances exploration and semantic object labeling. The proposed strategy was evaluated in a realistically simulated indoor environment, and results were benchmarked against other exploration strategies.

Urban search and rescue missions require rapid intervention to locate victims and survivors in the affected environments. To facilitate this activity, Unmanned Aerial Vehicles (UAVs) have been recently used to explore the environment and locate possible victims. In this paper, a UAV equipped with multiple complementary sensors is used to detect the presence of a human in an unknown environment. A novel human localization approach in unknown environments is proposed that merges information gathered from deep-learning-based human detection, wireless signal mapping, and thermal signature mapping to build an accurate global human location map. A next-best-view (NBV) approach with a proposed multi-objective utility function is used to iteratively evaluate the map to locate the presence of humans rapidly. Results demonstrate that the proposed strategy outperforms other methods in several performance measures such as the number of iterations, entropy reduction, and traveled distance.

In this paper, a Next Best View (NBV) approach with a profiling stage and a novel utility function for 3D reconstruction using an Unmanned Aerial Vehicle (UAV) is proposed. The proposed approach performs an initial scan in order to build a rough model of the structure that is later used to improve coverage completeness and reduce flight time. Then, a more thorough NBV process is initiated, utilizing the rough model in order to create a dense 3D reconstruction of the structure of interest. The proposed approach exploits the reflectional symmetry feature if it exists in the initial scan of the structure. The proposed NBV approach is implemented with a novel utility function, which consists of four main components: information theory, model density, traveled distance, and predictive measures based on symmetries in the structure. This system outperforms classic information gain approaches with a higher density, entropy reduction and coverage completeness. Simulated and real experiments were conducted and the results show the effectiveness and applicability of the proposed approach.

This paper explores the application of a real-time dynamic programming (RTDP) algorithm to transport a payload using a multi-rotor unmanned aerial vehicle (UAV) in order to optimize journey time and energy consumption. The RTDP algorithm is developed by discretizing the journey into distance interval horizons and applying the RTDP sweep to the current horizon to get the optimal velocity decision. RTDP sweep requires the current state of the UAV to generate the next best velocity decision. To the best of the authors knowledge, this is the first time that such real-time optimization algorithm is applied to multi-rotor based transportation. The algorithm was first tested in simulations and then experiments were performed. The results show the effectiveness and applicability of the proposed algorithm.