288 research outputs found
Synthesis, characterization and antimicrobial activities of some 5-bromouracilmetal ion complexes
Six new complexes, [Mn(Br-U)2(H2O)2]×4H2O (1), [Cd(Br-U)2]×2H2O (2), [Cu(Br-U)2(H2O)2]×2H2O (3), [Co(Br-U)2(H2O)2]×4H2O (4), [Ni(Br-U)2(H2O)2]×4H2O (5) and [Ag(Br-U)(Br-U-H)]×2(H2O) (6) were prepared by the reaction of 5-bromoouracil with MnCl2·4H2O, CdCl2·2.5H2O, CuSO4·5H2O, (CH3COO)2Co·4H2O, (CH3COO)2Ni·4H2O and AgNO3 respectively. The complexes were characterized by melting point, elemental microanalyses, IR and 1H NMR spectroscopy. The obtained data indicated that the ligand interacted with the metal ions in its mononegatively charged enol form in a bidentate fashion. Thermogravimetric analyses (TGA and DTG) were also carried out. The data obtained agreed well the proposed structures and showed that the complexes were finally decomposed to the corresponding metal or metal oxide. The ligand and its metal-ion complexes were tested for their antimicrobial activities against four bacterial strains (B. subtillis, S. aureus, E. coli and P. aeruginosa) by the agar-well diffusion technique using DMSO as a solvent. The obtained data showed that the complexes were more potent antimicrobial agents than the parent ligand. KEY WORDS: 5-Bromoouracil–M2+ complexes, IR, Thermal analyses, 1H NMR, Antimicrobial activity Bull. Chem. Soc. Ethiop. 2019, 33(2), 255-268.DOI: https://dx.doi.org/10.4314/bcse.v33i2.
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Power Optimization Analysis using Throughput Maximization in MISO Non-Orthogonal Multiple Access System
Planetary gearbox condition monitoring based on modulation analysis
The epicycle gearbox or planetary gearbox (PG) is a central power transmission systems of important machines such as helicopters and wind turbines which are mission critical and high cost systems. Condition monitoring (CM) has been explored extensively in recent years to avoid any unexpected interruptions and severe accidences caused by faults PGs. Although, considerable advancements in CM techniques, there still existed significant deficiency such as insensitivity, false diagnosis and high costs in implementing such techniques in industries. To improve CM techniques, therefore, this thesis focuses on an investigation of advanced signal analysis techniques such as higher order spectra (HOS) in order to achieve full characterisation of the nonlinear modulation processes of PG dynamics and thereby develop accurate diagnostic techniques.
The lumped mass model is established for modelling the dynamic behaviour of the PG under investigation, which allows the vibration behaviours to be understood for analysing different abnormalities such as tooth breakages and gear errors. This paves the way for subsequent data analytics and fault diagnostics using modulation signal bispectrum (MSB) that allows the vibration data to be examined through HOS, but it is significantly efficient in characterising the multiple and nonlinear modulations of PG dynamics alongside superior noise reduction performance.
Different degrees of misalignments in the PG drive system has been investigated and successfully diagnosed using MSB analysis of vibration measurements.. Moreover, the investigation included detection of tooth breakage faults of different severities in both the sun and a planet gear. The tooth faults were diagnosed using the recently developed MSB through accurately representation and estimate of residual sidebands induced by these faults. Consequently, MSB analysis produces an accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for wide operating conditions.
Furthermore, these fault diagnosis practices allows the establishment of residual sideband analysis approach. These residual sidebands resulting from the out-of-phase superposition of vibration waves due to asymmetric, multiple meshing sources are much less influenced by gear errors than the in-phase sidebands due to faults or new occurrences of the symmetricity. MSB can provide an accurate characterisation of the residual sidebands and consequently produces consistent diagnosis as confirmed by both simulation and experiment
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A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System
Data Availability Statement: Not applicable.Copyright © 2023 by the authors. In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.This research received no external funding
The correlation of in vitro antioxidant potentials with the various biochemical responses of salinized basil leaves
One of the environmental sustainability issues is salinity. Basil seedlings (Ocimum basilicum L.) were treated using NaCl solutions of three different concentrations prepared using irrigation (40, 80, and 130 mM), and various biochemical analyses were performed on basil leaves. The number of leaves, leaf area, moisture, weights, and MDA content of basil decreased significantly as salinity levels increased from 40 to 130 mM; however, dry matter increased. As well, the current study investigated a significant increase in osmolytes (including total soluble sugars and proline) and Na+ contents. The highest activities of CAT and SOD in the leaf tissues of basil were recorded after treatment with 130 mM NaCl, whereas the polyphenol and total flavonoid contents were negatively influenced. On the other hand, the highest ABTS scavenging activity was observed in the 40 mM-treated leaves at a concentration of 1000 µg/mL; however, the DPPH scavenging potential increased significantly in the 80 mM-treated leaves at 3000 µg/mL. Furthermore, the correlation between in vitro antioxidant potentials and biochemical responses was described. A strong correlation was identified between the in vitro antioxidant capacities of salinized O. basilicum leaves and SOD activity, total flavonoids, and the presence of phenolic acids, particularly p-hydroxybenzoic and o-coumaric acids at various concentrations. As a result, this is the first study to explain how basil may resist salinity by producing specific antioxidant compounds; therefore, our research recommends use of salinity issue to obtain a better plant material for producing dietary supplements or herbal drugs
Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System
Data Availability Statement: Not applicable.Copyright: © 2022 by the authors. In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.Funding: This research received no external funding
Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas
Purpose: To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas. Material and methods: A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas. Results: There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10-3 mm2/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%. Conclusion: TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas
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Extended Comparison and Performance Analysis for Mobile Ad-Hoc Networks Routing Protocols Based on Different Traffic Load Patterns and Performance Metrics
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.A mobile ad-hoc network (MANET) is a network of mobile nodes that dynamically form a transitory network lacking any existence of infrastructure and any form of centralized management. Nodes in ad hoc networks are powered by batteries with a limited lifespan and communicate in a restricted bandwidth. The unpredictable environment of a MANET may run into a major concern in the routing mechanism, therefore the need for a routing protocol with robust performance is still one of the key challenges in MANET deployment. In this work, a comparative comparison and extensive simulation analysis have been carried out for three major routing protocols: destination sequenced distance vector (DSDV), dynamic source routing (DSR) and ad hoc on-demand distance vector (AODV). Protocol evaluation has been extended by considering several simulation arrangements, different classes of traffic load patterns and diverse performance metrics. Based on packet rate change, node quantity and node speed, simulation scenarios were generated. Protocols were investigated against energy consumption, throughput, lost packets, routing load and packet delivery fraction for three types of traffic load patterns regular, irregular and joint traffic. DSR and AODV protocols proved to be more reliable when joint traffic was implemented when node speed and packets variations are considered. DSDV protocol verifies outstanding response over other protocols in terms of energy consumption when either regular or irregular traffic is applied. The simulation results for DSR protocol have verified the superiority over other protocols in 9 simulation scenarios when diverse metrics are considered. DSDV showed optimal performance in 7 cases, especially at low packet rates and in networks with minimum number of nodes. Similarly, AODV protocol showed outstanding performance in 6 scenarios, when higher packet rates and node mobility are considered.This research received no external funding
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