Evaluation of Antimicrobial Activity of a Sudanese Herbal Plant (Piliostigma reticulatum)

Background: Piliostigma reticulatum is a plant that is found in a wide area of SaheloSudanian region of Africa. It is widely used in Africa as a traditional medicine for the treatment of a wide range of diseases including epilepsy, anxiety, and agitation. The leaf extract was found to have antimicrobial activity. In Sudan (Nuba mountains in particular), it is widely used to dress new wounds and as well puerperal sepsis.Moreover it’s fruit is eaten and used to prepare juice. Reported studies concerning antimicrobial activity of the plant in Sudan could not be found. This study therefore aimed to evaluate the antimicrobial action of Ethanolic and Aqueous extract of leaves and barks of the plant. Methods: Barks and leaves of P. reticulatum were obtained from North Kordofan State. They were then air dried in the shade and milled into powder using Mortar. Methanolic and water extract of each part of the plant was prepared using a Soxhlet apparatus. The following concentrations of extracts of each part (bark and leaves)of the plant were prepared using Distilled water (50 mg/ml, 25 mg/ml, 12.5 mg/ml, 6.25 mg/ml, 3.125 mg/ml, and 1.56 mg/ml). Antimicrobial action of the different concentrations of the extracts of the two parts of the plant on selected bacterial and fungal species was performed using well diffusion technique. Antimicrobialsusceptibility of the tested organisms to serial concentrations (40 µg, 20 µg, 10 µg, and 5 µg) of three antibacterial (Gentamicin, Ampicillin, and Tetracycline) and 2 antifungal (Nystatin and clotrimazole) was evaluated using well diffusion method. Results: The methanolic extract of P. reticulatum leaves showed high antibacterial activity against Bacillus subtilis (inhibition zone 22 mm), S. aureus (25 mm), P.aeruginosa (23 mm), and E.coli (20 mm). The extract also showed antifungal activity against A. niger (23 mm) and C. albicans (23 mm). The aqueous extract revealed low activity against P. aeruginosa (10 mm) and no action on the rest of the microorganisms

Prospects and Challenges of Green Hydrogen Economy via Multi-Sector Global Symbiosis in Qatar

Low carbon hydrogen can be an excellent source of clean energy, which can combat global climate change and poor air quality. Hydrogen based economy can be a great opportunity for a country like Qatar to decarbonize its multiple sectors including transportation, shipping, global energy markets, and industrial sectors. However, there are still some barriers to the realization of a hydrogen-based economy, which includes large scale hydrogen production cost, infrastructure investments, bulk storage, transport & distribution, safety consideration, and matching supply-demand uncertainties. This paper highlights how the aforementioned challenges can be handled strategically through a multi-sector industrial-urban symbiosis for the hydrogen supply chain implementation. Such symbiosis can enhance the mutual relationship between diverse industries and urban planning by exploring varied scopes of multi-purpose hydrogen usage (i.e., clean energy source as a safer carrier, industrial feedstock and intermittent products, vehicle and shipping fuel, and international energy trading, etc.) both in local and international markets. It enables individual entities and businesses to participate in the physical exchange of materials, by-products, energy, and water, with strategic advantages for all participants. Besides, waste/by-product exchanges, several different kinds of synergies are also possible, such as the sharing of resources and shared facilities. The diversified economic base, regional proximity and the facilitation of rules, strategies and policies may be the key drivers that support the creation of a multi-sector hydrogen supply chain in Qatar. Copyright 2021 Eljack and Kazi.This paper was made possible by NPRP grant no. 10-0205-170347 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu

Design of composite rectangular tubes for optimum crashworthiness performance via experimental and ANN techniques

This paper examines the crashworthiness performance of composite rectangular tubes using experimental and artificial neural network (ANN) techniques. Based on experimentally obtained values of different crashworthiness parameters under various loading conditions, ANN models are constructed to identify the optimum cross-sectional aspect ratio of cotton fiber/epoxy laminated composite to achieve the targeted mechanical properties such as load carrying and energy absorption capability. Experimental findings show that axially and laterally loaded rectangular tubes were significantly affected by their aspect ratio. Furthermore, the predictions obtained from the ANN models showed consistency with the experimental data. In addition, the developed ANN captured the complicated nonlinear relationship among crashworthiness parameters to obtain insight into the practical design of the composite materials. 2021 The Author(s)This paper was made possible by NPRP grant No 10-0205-170347 from the Qatar National Research Fund (a member of the Qatar Foundation).Scopu

Safety and immunogenicity of an autoclaved Leishmania major vaccine

Objective: To test the safety and immunogenicity of two doses of autoclaved L.major (ALM) vaccine mixed with BCG.Setting: Kala-azar endemic area of eastern Sudan.Design: This was a randomised, double blind and BCG controlled phase I/II study.Subjects: Eighty healthy volunteers (forty children and forty adults) with no past history of kala-azar, no reactivity to leishmanin antigen and with a reciprocal direct agglutination test (DAT) titre o

Molecular Property Clustering Techniques

In this work, systematic property clustering methods for molecular design are described. Group contribution first-order estimation methods are coupled with property clustering methods to define the targets of the property search space and to predict the properties of the synthesized formulations. In the visual property clustering approach, the property constraints on the molecular synthesis problem are transformed into a feasibility region on a ternary property clustering diagram. Molecular building blocks are represented as points on the diagram, and the synthesis of candidate formulations is achieved by adding molecular fragments using linear mixing rules. Candidate formulations must satisfy necessary and sufficient conditions. The visual clustering methods are limited to problems that can be defined by three properties. For all other cases, the algebraic molecular clustering approach is capable of lowering the complexity of the molecular design problem. Proof-of-concept examples are used to demonstrate the tools.Scopu

Practicality of Green H2Economy for Industry and Maritime Sector Decarbonization through Multiobjective Optimization and RNN-LSTM Model Analysis

A green H2 economy's practicality depends highly on multisector decarbonization and cost-benefit analyses of the supply chain, including production routes, techno-economic performance, storage, and transportation. In this work, a strategic methodology is used to formulate the multiobjective optimization problem as a mixed-integer linear programming (MILP) to optimize green H2 economy scenarios for decarbonizing the industrial and marine sectors concurrently. GAMS/IBM ILOG CPLEX 30.3.0 solver was used to solve the MILP, and an RNN-LSTM prediction model was used to identify the future H2 demand from the maritime sector. Capital budgeting and sensitivity analysis were conducted for a constant 5% industrial sector decarbonization target and variable scenario-based maritime sector decarbonization. The results provide the detailed cost-benefit and trade-offs analysis for the optimal supply chain elements. The findings of this study can help define the essential legislative changes required to promote green H2 as a marine fuel. 2022 American Chemical Society.This paper was made possible by NPRP Grant No. 10-0205-170347 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu

Data-driven modeling to predict the load vs. displacement curves of targeted composite materials for industry 4.0 and smart manufacturing

This work presents an approach for smart manufacturing focusing on Industry 4.0 to predict the load vs. displacement curve of targeted cotton fiber/Polypropylene (PP) composite materials while complying with the required intended properties. Experimental data for varying composite fiber percentage to characteristic load and earlier built artificial neural network (ANN) models are used as the feed. A newly built ANN model is being trained and tested on the TensorFlow backend using the Keras library in Python to predict the load vs. displacement curves for any in-between values of the experimental range (e.g., 0-50% cotton fiber filler content in PP) without doing any further experiment. Finally, a Python package for the sparse identification of nonlinear dynamical (PySINDy) systems is used to identify the exact data-driven ANN model through the system identification, which will facilitate the effective implementation of the control algorithms, smart internet of things (IoT), and high-tech automated system. 2020 Elsevier LtdThis paper was made possible by NPRP grant No 10-0205-170347 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s].Scopu

Synthesis of water capture technologies for gas fired power plants in Qatar

Flue gas from gas fired power plants contains 10-16% (w/w) water vapor with considerable amount of latent heat. Although CO2 capture and utilization have received great attention, water capture from power plant has received limited attention. The power plants in Qatar exhaust 33 Million m3 of water per year. This paper explores selected alternative technologies namely absorption, compression & cooling, and quenching, to enable the recovery of water vapor contained in a base case 750 MW power plant flue gas streams. The alternatives for water capture were modeled and optimized over a wide range of operating conditions (pressure, temperature, and flow rate). Using data from an actual gas fired power plant in the state of Qatar, simulation studies were carried out and optimized for all modeled technologies to minimize production cost using Aspen HYSYS V8.6. The results show that the quench unit, operated at pertinent water circulation temperature (50 C), pressure (6 atm), and flowrate of 3500 m3/h (recyclable), can extract up to 80.7% of the water in the flue gas. Apart from production cost and water capture percentage, criteria used to screen the alternative technologies were payback period, CO2e emission and brine reduction rate. The research work determined that the quench alternative had the lowest payback period (8.8 years), lowest CO2 emission rate (13 kg CO2/m3 H2O) and highest brine reduction (3.44%) among all the tested alternatives. The proposed quench water-recovery technology will have added value to Qatar and other nations with limited water resources, specifically those with access to natural gas resources. 2019The authors would like to thank Dr. Mohammad Amanullah for his continuous support during the project. This paper was made possible by Qatar University grant No QUUG-CENG-CHE-15/16-2. The statements made herein are solely the responsibility of the author[s].Scopu

Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network

In this paper, a machine learning-based approach has been proposed to integrate artificial intelligence during the designing of fiber-reinforced polymeric composites. With the help of the proposed approach, an artificial neural network (ANN) model has been developed to achieve the targeted filler content for cotton fiber/polypropylene composite while satisfying the required targeted properties. Previously obtained experimental data sets were trained on the TensorFlow backend using Keras library in Python, followed by hyperparameter tuning and k-fold cross-validation method for acquiring a better performing model to predict the amount of targeted filler content. The developed approach proved to be very efficient and reduced the time and effort of the material characterization for numerous samples, and it will help materials designers to design their future experiments effectively. The developed approach in this paper can be extended for other composite materials if the necessary experimental data are available to train the ANN model. 2020 Elsevier LtdThis paper was made possible by NPRP grant No 10-0205-170347 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu