OPTIMAL WATER QUALITY MANAGEMENT STRATEGIES FOR URBAN WATERSHEDS USING MACRO-LEVEL SIMULATION MODELS LINKED WITH EVOLUTIONARY ALGORITHMS

Urban watershed management poses a very challenging problem due to the varioussources of pollution and there is a need to develop optimal management models that canfacilitate the process of identifying optimal water quality management strategies. Ascreening level, comprehensive, and integrated computational methodology is developedfor the management of point and non-point sources of pollution in urban watersheds. Themethodology is based on linking macro-level water quality simulation models withefficient nonlinear constrained optimization methods for urban watershed management.The use of macro-level simulation models in lieu of the traditional and complexdeductive simulation models is investigated in the optimal management framework forurban watersheds. Two different types of macro-level simulation models are investigatedfor application to watershed pollution problems namely explicit inductive models andsimplified deductive models. Three different types of inductive modeling techniques areused to develop macro-level simulation models ranging from simple regression methodsto more complex and nonlinear methods such as artificial neural networks and geneticfunctions. A new genetic algorithm (GA) based technique of inductive modelconstruction called Fixed Functional Set Genetic Algorithm (FFSGA) is developed andused in the development of macro-level simulation models. A novel simplified deductivemodel approach is developed for modeling the response of dissolved oxygen in urbanstreams impaired by point and non-point sources of pollution. The utility of this inverseloading model in an optimal management framework for urban watersheds isinvestigated.In the context of the optimization methods, the research investigated the use of parallelmethods of optimization for use in the optimal management formulation. These includedan evolutionary computing method called genetic optimization and a modified version ofthe direct search method of optimization called the Shuffled Box Complex method ofconstrained optimization. The resulting optimal management model obtained by linkingmacro-level simulation models with efficient optimization models is capable ofidentifying optimal management strategies for an urban watershed to satisfy waterquality and economic related objectives. Finally, the optimal management model isapplied to a real world urban watershed to evaluate management strategies for waterquality management leading to the selection of near-optimal strategies

Catalytic Reductive Degradation of Methyl Orange Using Air Resilient Copper Nanostructures

The study describes the application of oxidation resistant copper nanostructures as an efficient heterogeneous catalyst for the treatment of organic dye containing waste waters. Copper nanostructures were synthesized in an aqueous environment using modified surfactant assisted chemical reduction route. The synthesized nanostructures have been characterized by UV-Vis, Fourier transform infrared spectroscopy FTIR spectroscopy, Atomic force microscopy (AFM), Scanning Electron Microscopy (SEM), and X-ray diffractometry (XRD). These surfactant capped Cu nanostructures have been used as a heterogeneous catalyst for the comparative reductive degradation of methyl orange (MO) in the presence of sodium borohydride (NaBH4) used as a potential reductant. Copper nanoparticles (Cu NPs) were found to be more efficient compared to copper nanorods (Cu NRds) with the degradation reaction obeying pseudofirst order reaction kinetics. Shape dependent catalytic efficiency was further evaluated from activation energy (EA) of reductive degradation reaction. The more efficient Cu NPs were further employed for reductive degradation of real waste water samples containing dyes collected from the drain of different local textile industries situated in Hyderabad region, Pakistan

Identification of novel and safe fungicidal molecules against fusarium oxysporum from plant essential oils: in vitro and computational approaches.

Phytopathogenic fungi are serious threats in the agriculture sector especially in fruit and vegetable production. The use of plant essential oil as antifungal agents has been in practice from many years. Plant essential oils (PEOs) of Cuminum cyminum, Trachyspermum ammi, Azadirachta indica, Syzygium aromaticum, Moringa oleifera, Mentha spicata, Eucalyptus grandis, Allium sativum, and Citrus sinensis were tested against Fusarium oxysporum. Three phase trials consist of lab testing (MIC and MFC), field testing (seed treatment and foliar spray), and computer-aided fungicide design (CAFD). Two concentrations (25 and 50 μl/ml) have been used to asses MIC while MFC was assessed at four concentrations (25, 50, 75, and 100 μl/ml). C. sinensis showed the largest inhibition zone (47.5 and 46.3 m2) for both concentrations. The lowest disease incidence and disease severity were recorded in treatments with C. sinensis PEO. Citrus sinensis that qualified in laboratory and field trials was selected for CAFD. The chemical compounds of C. sinensis PEO were docked with polyketide synthase beta-ketoacyl synthase domain of F. oxysporum by AutoDock Vina. The best docked complex was formed by nootkatone with -6.0 kcal/mol binding affinity. Pharmacophore of the top seven C. sinensis PEO compounds was used for merged pharmacophore generation. The best pharmacophore model with 0.8492 score was screened against the CMNP database. Top hit compounds from screening were selected and docked with polyketide synthase beta-ketoacyl synthase domain. Four compounds with the highest binding affinity and hydrogen bonding were selected for confirmation of lead molecule by doing MD simulation. The polyketide synthase-CMNPD24498 showed the highest stability throughout 80 ns run of MD simulation. CMNPD24498 (FW054-1) from Verrucosispora was selected as the lead compound against F. oxysporum

Exogenous Melatonin Improves Cold Tolerance of Strawberry (Fragaria × ananassa Duch.) through Modulation of DREB/CBF-COR Pathway and Antioxidant Defense System

The strawberry (Fragaria × ananassa Duch.) is an important fruit crop cultivated worldwide for its unique taste and nutritional properties. One of the major risks associated with strawberry production is cold damage. Recently, melatonin has emerged as a multifunctional signaling molecule that influences plant growth and development and reduces adverse consequences of cold stress. The present study was conducted to investigate the defensive role of melatonin and its potential interrelation with abscisic acid (ABA) in strawberry plants under cold stress. The results demonstrate that melatonin application conferred improved cold tolerance on strawberry seedlings by reducing malondialdehyde and hydrogen peroxide contents under cold stress. Conversely, pretreatment of strawberry plants with 100 μM melatonin increased soluble sugar contents and different antioxidant enzyme activities (ascorbate peroxidase, catalase, and peroxidase) and non-enzymatic antioxidant (ascorbate and glutathione) activities under cold stress. Furthermore, exogenous melatonin treatment stimulated the expression of the DREB/CBF—COR pathways’ downstream genes. Interestingly, ABA treatment did not change the expression of the DREB/CBF—COR pathway. These findings imply that the DREB/CBF-COR pathway confers cold tolerance on strawberry seedlings through exogenous melatonin application. Taken together, our results reveal that melatonin (100 μM) pretreatment protects strawberry plants from the damages induced by cold stress through enhanced antioxidant defense potential and modulating the DREB/CBF—COR pathway. View Full-Tex

Application of Artificial Intelligence in Medical Education: Current Scenario and Future Perspectives

Introduction: Medical education is a lifetime learning process stretching from undergraduate to postgraduate, specialty training, and beyond. It also applies to various healthcare professionals, including doctors, nurses, and other allied healthcare professionals. Therefore, it is essential to acknowledge the immense role of artificial intelligence in medical education in the current era of rapidly growing technology.Methods: High-quality data that met the study objectives were included. In addition, comprehensive investigations on articles available in reputable databases such as PubMed, Research Gate, PubMed central, Web of Science, and Google Scholar were considered for literature review.Results: Artificial intelligence has fixed various issues in education during the last decade, including language processing,reasoning, planning, and cognitive modelling.Conclusion: It can be used in medical education in the following forms: Virtual Inquiry System, Medical Distance Learning and Management, and Recording teaching videos in medical schools. It can also enhance the value of the non-analytical humanistic aspects of medicine. The goal of this review article was to present the implications of AI in medical education, now and in the coming years

Forecasting Impact of Demand Side Management on Malaysia’s Power Generation using System Dynamic Approach

Rapid economic growth, increasing population, industrialization and high living standards have increased the electricity demand more than ever before. Efficient energy planning and management is always considered as the greatest challenge in all over the world. Among the other factors availability of electricity is the main bottleneck to the economic growth and industrial revolution. Considering this fact, it becomes necessary for academicians, government agencies and electricity companies to construct more efficient methodologies and procedures to predict long-term electricity demand. The objective of this article represents the initiative towards understanding and analyzing the importance of demand-side management (DSM) in forecasting electricity demand by using a system dynamics approach. This study examines the long term impact of demand-side management variables including HER (Home energy report), MEPS (Minimum Energy Performance Standards) and NEEAP (National Energy Efficiency Action Plan). The future installation capacity of Malaysia’s power generation is evaluated considering the factors of population, per capita electricity consumption, efficiency, capacity margin and DSM. The forecasting horizon of the simulation model is 15 years from 2016 to 2030.Keywords: Energy forecasting, System Dynamics, Energy efficiency, Energy Demand Side ManagementJEL Classifications: O18, Q21DOI: https://doi.org/10.32479/ijeep.9716</p

2D nanostructures: Potential in diagnosis and treatment of Alzheimer's disease

Two-dimensional (2D) nanomaterials have garnered enormous attention seemingly due to their unusual architecture and properties. Graphene and graphene oxide based 2D nanomaterials remained the most sought after for several years but the quest to design superior 2D nanomaterials which can find wider application gave rise to development of non-graphene 2D materials as well. Consequently, in addition to graphene based 2D nanomaterials, 2D nanostructures designed using macromolecules (such as DNAs, proteins, peptides and peptoids), transition metal dichalcogenides, transition-metal carbides and/or nitrides (MXene), black phosphorous, chitosan, hexagonal boron nitrides, and graphitic carbon nitride, and covalent organic frameworks have been developed. Interestingly, these 2D nanomaterials have found applications in diagnosis and treatment of various diseases including Alzheimer’s disease (AD). Although AD is one of the most debilitating neurodegenerative conditions across the globe; unfortunately, there remains a paucity of effective diagnostic and/or therapeutic intervention for it till date. In this scenario, nanomaterial-based biosensors, or therapeutics especially 2D nanostructures are emerging to be promising in this regard. This review summarizes the diagnostic and therapeutic platforms developed for AD using 2D nanostructures. Collectively, it is worth mentioning that these 2D nanomaterials would seemingly provide an alternative and intriguing platform for biomedical interventions