With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model ...called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called
warm
ptimized
on-dominated sorting
enetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay-energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.
Originating in China in December 2019, coronavirus disease 2019 (COVID-19) rapidly spread to more than 216 countries in the world by May 2020. Because dentists are at a higher risk of contracting the ...disease, the present study assessed the fear and anxiety among dental practitioners of becoming infected with COVID-19.
An online cross-sectional questionnaire survey comprising of 9 questions was conducted among dental practitioners of Telangana. Age, gender, qualification, type of practice, years of practice, and place of residence were the demographic variables recorded. The response to each question was recorded in a YES or NO format, and mean fear score was calculated to categorize answers into low and high levels of fear. Comparison of mean fear score was done using t-test for 2 variables and analysis of variance for 3 or more than 3 variables. Multiple logistic regression analysis of the levels of fear with demographic variables was done.
< 0.05 was considered statistically significant.
The mean fear and anxiety score of this study population reported was high 6.57 ± 2.07, with 58.31% of the population presenting with a low level of fear and anxiety. Only qualification (
= 0.045) and gender (
= 0.035) revealed a significant difference in fear to Q7and Q8, respectively. Irrespective of the age, gender, qualification, type of practice, and years in practice, the levels of fear reported in the present study were very similar. Respondents between 41 and 60 y of age (6.70 ± 2.01 y) and those with individual practices (6.70 ± 2.06 y) exhibited high fear scores.
The present study demonstrates cross-sectional data of fear and anxiety among dental practitioners during the COVID-19 outbreak. Heightened levels of fear observed in this study call for a nationwide analysis of fear among dentists and deliberate management strategies for the same.
•Development of a hybrid GA-ANN model to predict the laminar burning velocity of iso-octane/blends-air mixtures.•The present study considers eleven blends including zero-carbon fuels, alcohols, and ...oxygenated fuels.•Developed ANN model outperformed the other LBV calculation methods in computational time as well as accuracy.•A general correlation is developed for isooctane/blends-air mixtures over a wide range of operating conditions and a good prediction accuracy was obtained.
Surrogate fuels offer a cost-effective way to predict the combustion properties of transportation fuels like diesel, gasoline, kerosene, etc. Iso-octane (2,2,4-trimethylpentane) is a key gasoline reference and surrogate component. Researchers explore alternative fuels and their combustion characteristics to enhance efficiency and reduce emissions. The rising interest lies in the blending of isooctane with various alternative fuels, aiming for cleaner and more efficient combustion. In this current study, a machine learning method called Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) was employed to forecast the laminar burning velocity (LBV) of isooctane/blends – air mixtures. A total of eleven blends including ammonia, hydrogen, methane, methanol, ethanol, butanol, n-heptane, 2-methyl furan (2-MF), 2,5-dimethylfuran (2,5-DMF), 2-methyl tetrahydrofuran (2-MTHF), and syngas were examined. The artificial neural network (ANN) model was created using a dataset consisting of 2234 data points gathered from the past experimental literature since 1983. To enhance the ANN's predictive capability, a combination of the random search CV technique and selective testing approach was utilized for optimizing ANN hyperparameters, while the genetic algorithm (GA) was deployed to optimize the ANN's weight values. The development of the ANN model was carried out within the Python software environment, utilizing the Keras application programming interface. The constructed GA-ANN model was compared to a variety of other machine learning (ML) models developed within this study, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and XGBoost regression. When evaluated on the testing set, which constitutes 15% of the complete dataset, the GA-ANN model demonstrated superior performance compared to all other ML models utilized in this research, achieving an impressive prediction accuracy with the coefficient of determination (R2) of 0.9910, root mean square error (RMSE) of 0.8231, and a mean absolute error (MAE) of 0.643. Additionally, a common LBV correlation for all isooctane/blend-air mixtures was created using the extended Gulder’s LBV formulation with the input parameters including pressure, temperature, equivalence ratio, and molar fraction of blend. This correlation results showed very minimum deviation from the experimental with an RMSE value of less than 0.38541.
Usage of natural fibers as reinforcing materials in composites has increased in recent years, due to their exceptional advantages like lightweight, low cost and biodegradable. To overcome the ...drawbacks in using the natural fibers due to the lack of bonding, higher moisture absorption and lower melting point, surface modification by alkali treatment on prosopis juliflora fibers with different weight concentrations of NaOH (5%, 10% and 15%) were carried out in the present work. These fibers have been used as reinforcements in an epoxy matrix. The outcomes of fiber alteration along with different weight proportions (10, 15, 20 and 25 wt%) of prosopis juliflora on the mechanical and water absorption properties were investigated. The outcomes of fiber-loading and alkali treatments on the aforementioned properties were investigated. The alkali behavior of fiber attained better results by improving the bond between the fiber surface and polymer matrix and also increased the fiber strength. The water absorption test showed that NaOH-treated fiber composites reveal a smaller amount of water absorption than untreated prosopis juliflora fiber-reinforced composites. The alkali conducts effect on the prosopis juliflora fibers have been distinguished by means of infrared spectroscopy (FTIR), thermo-gravimetric analysis (TGA) study and microscopy analysis (SEM).
The objective of this research was to map quantitative trait loci (QTLs) of multiple traits of breeding importance in pea (Pisum sativum L.). Three recombinant inbred line (RIL) populations, PR-02 ...(Orb x CDC Striker), PR-07 (Carerra x CDC Striker) and PR-15 (1-2347-144 x CDC Meadow) were phenotyped for agronomic and seed quality traits under field conditions over multiple environments in Saskatchewan, Canada. The mapping populations were genotyped using genotyping-by-sequencing (GBS) method for simultaneous single nucleotide polymorphism (SNP) discovery and construction of high-density linkage maps.
After filtering for read depth, segregation distortion, and missing values, 2234, 3389 and 3541 single nucleotide polymorphism (SNP) markers identified by GBS in PR-02, PR-07 and PR-15, respectively, were used for construction of genetic linkage maps. Genetic linkage groups were assigned by anchoring to SNP markers previously positioned on these linkage maps. PR-02, PR-07 and PR-15 genetic maps represented 527, 675 and 609 non-redundant loci, and cover map distances of 951.9, 1008.8 and 914.2 cM, respectively. Based on phenotyping of the three mapping populations in multiple environments, 375 QTLs were identified for important traits including days to flowering, days to maturity, lodging resistance, Mycosphaerella blight resistance, seed weight, grain yield, acid and neutral detergent fiber concentration, seed starch concentration, seed shape, seed dimpling, and concentration of seed iron, selenium and zinc. Of all the QTLs identified, the most significant in terms of explained percentage of maximum phenotypic variance (PV
) and occurrence in multiple environments were the QTLs for days to flowering (PV
= 47.9%), plant height (PV
= 65.1%), lodging resistance (PV
= 35.3%), grain yield (PV
= 54.2%), seed iron concentration (PV
= 27.4%), and seed zinc concentration (PV
= 43.2%).
We have identified highly significant and reproducible QTLs for several agronomic and seed quality traits of breeding importance in pea. The QTLs identified will be the basis for fine mapping candidate genes, while some of the markers linked to the highly significant QTLs are useful for immediate breeding applications.
Oxidative stress, resulting from the excessive intracellular accumulation of reactive oxygen species (ROS), reactive nitrogen species (RNS), and other free radical species, contributes to the onset ...and progression of various diseases, including diabetes, obesity, diabetic nephropathy, diabetic neuropathy, and neurological diseases, such as Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and Parkinson’s disease (PD). Oxidative stress is also implicated in cardiovascular disease and cancer. Exacerbated oxidative stress leads to the accelerated formation of advanced glycation end products (AGEs), a complex mixture of crosslinked proteins and protein modifications. Relatively high levels of AGEs are generated in diabetes, obesity, AD, and other I neurological diseases. AGEs such as Ne-carboxymethyllysine (CML) serve as markers for disease progression. AGEs, through interaction with receptors for advanced glycation end products (RAGE), initiate a cascade of deleterious signaling events to form inflammatory cytokines, and thereby further exacerbate oxidative stress in a vicious cycle. AGE inhibitors, AGE breakers, and RAGE inhibitors are therefore potential therapeutic agents for multiple diseases, including diabetes and AD. The complexity of the AGEs and the lack of well-established mechanisms for AGE formation are largely responsible for the lack of effective therapeutics targeting oxidative stress and AGE-related diseases. This review addresses the role of oxidative stress in the pathogenesis of AGE-related chronic diseases, including diabetes and neurological disorders, and recent progress in the development of therapeutics based on antioxidants, AGE breakers and RAGE inhibitors. Furthermore, this review outlines therapeutic strategies based on single-atom nanozymes that attenuate oxidative stress through the sequestering of reactive oxygen species (ROS) and reactive nitrogen species (RNS).
In this work, the electrochemical performance of NiFe2O4 nanofibers synthesized by an electrospinning approach have been discussed in detail. Lithium storage properties of nanofibers are evaluated ...and compared with NiFe2O4 nanoparticles by galvanostatic cycling and cyclic voltammetry studies, both in half-cell configurations. Nanofibers exhibit a higher charge-storage capacity of 1000 mAh g–1 even after 100 cycles with high Coulmbic efficiency of 100 % between 10 and 100 cycles. Ex situ microscopy studies confirmed that cycled nanofiber electrodes maintained the morphology and remained intact even after 100 charge–discharge cycles. The NiFe2O4 nanofiber electrode does not experience any structural stress and eventual pulverisation during lithium cycling and hence provides an efficient electron conducting pathway. The excellent electrochemical performance of NiFe2O4 nanofibers is due to the unique porous morphology of continuous nanofibers.
A stereoselective synthesis of decahydrofuro3,2-disochromene derivatives has been achieved by the condensation of 2-cyclohexenylbutane-1,4-diol with aldehydes in the presence of a stochiometric ...amount of BF3·OEt2 in dichloromethane at −78 °C. Similarly, the condensation of 2-cyclopentenylbutan-1,4-diol with aldehydes provides the corresponding octahydro-2H-cyclopentacfuro2,3-dpyran derivatives in good yields with high diastereoselectivity. It is an elegant strategy for the quick construction of tricyclic architectures with four contiguous stereogenic centers in a single step. These tricyclic frameworks are the integral part of numerous natural products.
A vital element of widespread patient monitoring is consistency in transmission between the patients and the healthcare professionals not including time and position dependencies. Artificial ...intelligence (AI) and machine learning (ML) techniques have a vast possibility to proficiently handle the automated function of the mobile nodes distributed in the Mobile ad-hoc network (MANET). ML is a part of AI in that the computer algorithms learn themselves by improving from historical experiences. The main issues in MANET are autonomous operation, maximization of a lifetime, coverage of the network, energy utilization, connectivity issues, quality of service, high bandwidth necessity, communication protocol design, etc. ML is valuable for data aggregation, and it saves the energy of mobile nodes and enhances the network lifetime. In this paper, we propose an Artificial Intelligence Machine Learning Algorithm for improving the Quality of Service in MANET (AIMQ). ML techniques based on artificial neural networks (ANN) algorithm is helpful for data aggregation tasks. This approach formed the clusters by node mobility and connectivity. Glowworm Swarm Optimization (GSO) algorithm is applied in every cluster to choose a proficient Cluster Head (CH). Here, we choose the CH by GSO fitness function based on mobile node degree, node distance, node reliability, and energy. ANN algorithm recognizes and chooses the data aggregator with great energy and more extended stability. It updates the weight of input parameters such as node energy, node degree, packet loss ratio, and node delay to reduce the errors. It minimizes the repetitive CHs selection and member nodes' re-affiliation in a cluster.
•Cationic ordering and the element-specific magnetic moments.•Ferromagnetic coupling of Gd3+ and Dy3+ substituted ions to the Fe3+/Ni2+ at B-site.•Collinear ferrimagnetic structure with the partially ...inverted spinel structure.
Cationic distribution and magnetic properties of partially rare-earth (R) doped NiFe2O4 (NFO) compounds, i.e. NiFe2-xRxO4 (x = 0 and 0.075; R = Gd, Dy and Ho) were investigated, and the results are discussed and presented in this paper. All the compounds were found to stabilize in the cubic inverse spinel structure with the space group Fd3-m. The analysis of the results based on X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) measurements are primarily focused on the valence state information of constituting rare-earth or transition-metal ions, cationic distribution, and the coupling between their magnetic moments. The XAS data revealed that nickel is in Ni2+ valence state while iron is mainly in the Fe3+ valence state though some perceptible traces of Fe2+ were observed. The rare-earth ions are in trivalent states. The partial doping causes differences in the cationic distribution of Ni2+ and Fe3+ ions which obviously results in differences in the net Fe or Ni magnetic moments. Furthermore, 57Fe Mössbauer spectra revealed altered hyperfine field parameters upon the Gd3+, Dy3+ and Ho3+ substitution and all the compounds were found to exhibit collinear ferrimagnetic structures. The results are relevant for the investigation of orientation of individual magnetic moments and to obtain site-specific information related to all the cations involved in the rare-earth doped nickel ferrite structures.
PDF
