Continuous increase in world’s population, rapid industrialization, urbanization, and economic growth force for continuously increase in fossil fuel consumption to meet growing energy demand. ...Continuous emissions from burning of fossil fuel will create the need to find the appropriate and sustainable replacement for fossil fuels. Biodiesel is appropriate alternate solution for diesel engine due to its renewable, non-toxic and eco-friendly nature. According to EASAC biodiesel evolution is classified into four generations. Cultivation in arid and semi arid land or water, crop yield, effect on food supply, yield of biodiesel, energy content, carbon-neutral economy, easy availability, and economic viable are the main factors behind the evolution of biodiesel generations. This article highlights a comprehensive assessment of various feedstocks used for different generation biodiesel production with their advantages and disadvantages. Different production methods for biodiesel with yield calculation are also explained. Algae based third generation feedstocks are better in comparison with first and second generation due to their high energy content, high oil content and less polluting nature. Forth generation of biodiesel produced from synthetic biology, which will enhance the various physiochemical properties of biodiesel to achieve carbon neutral economy. Among the all biodiesel production processes; transesterification is the most suitable process, because it produces biodiesel of high yield, comparable properties with diesel. This process is also feasible as per economic point of view. The energy demand of future can be met by the blending of different generation oil feedstocks.
•Fake news detection using 26 linguistic features.•Three feature extraction techniques named Tf-idf, count-vectorizer and hash vectorizer is applied.•Artificial intelligence solution is used in ...social media and fact-checking industry.•Machine-learning models are implemented on four popular datasets.
Social media platforms now a day are mainly used for news consumption among users. Political groups use social media platforms to attract users by enclosing users' votes in their favor. Due to the large volume of data on social media, it is essential to verify the authenticity of the content. The use of artificial intelligence techniques including the development of embedding and deployment of the machine-learning algorithm is required to combat misinformation. This paper focused on various categories of linguistic features covering complexity features, readability index, psycholinguistic features, and stylometric features for competent fake news identification. The linguistic model helps in computing language-driven features by learning the properties of news content. In this work, we have selected twenty-six significant features and applied various machine learning models for implementation. For feature extraction, three different techniques named term frequency-inverse document frequency (tf-idf), count vectorizer (CV), and hash-vectorizer (HV) are applied. Then, we tested those models in different training dataset sizes to obtain accuracy for each model and compared them. We used four existing datasets for the experiment. The proposed framework achieved 90.8 % accuracy using Reuter dataset. Buzzfeed dataset obtained highest of 90% accuracy. Random Political and Mc_Intire dataset achieved an accuracy of 93.8 and 86.9% respectively.
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•Kaempferol is explored for its inhibitory effect on RhoA activation in-vitro.•Inflammatory cytokines and total ROS levels were reduced by kaempferol treatment.•Increased expression ...of TGF-β1, CTGF, fibronectin, collagen-IV in HG was reversed by Kaempferol.•Expression of E- cadherin was decreased in HG while kaempferol reversed this.
RhoA/Rho-associated coiled-coil forming protein serine/threonine kinase (ROCK) has appeared as a potential therapeutic target in numerous diseases, because of its preventing action on various enzymes providing antioxidant and cytoprotective action. Progression and pathophysiology of diabetic nephropathy have also shown potential involvement of oxidative stress and inflammatory pathways. In the present study, we investigated the effect of kaempferol on hyperglycemia-induced activation of RhoA kinase and associated inflammatory signaling cascade. Currently there is only small literature available on the mechanism of anti-diabetic and nephroprotective action of this compound, which creates a void. Therefore, we focused here on the investigation of molecular mechanisms for kaempferol by means of in vitro testing, using rat (NRK-52E) and human renal tubular epithelial cells (RPTEC). Our findings suggest that kaempferol inhibits hyperglycemia-induced activation of RhoA and decreased oxidative stress, pro-inflammatory cytokines (TNF-α and IL-1β) and fibrosis (TGF-β1 expression, extracellular matrix protein expression) in NRK-52E and RPTEC cells. Therefore, kaempferol can be used as a potential therapeutic for the treatment of diabetic nephropathy.
In the present analysis 15 experiments were performed in conjunction with the Box-Behnken architecture matrix based on the machining parameter's effect, like spindle speed, feed rate, and cutting ...width., A surface roughness mathematically framework was designed using the surface reaction methods of this model to aid a genetic algorithm. Which is used to decide the optimum machining parameters. Response surface methodology has been used in this paper due to certain advantages as compare to other methodology such as it needs fewer experiments to study the effects of all the factors and the optimum combination of all the variables can be revealed. Finally, a genetic algorithm was used to determine the optimum setting of process parameters that maximize the rate of content removal. The best surface roughness response value obtained from single-objective genetic algorithm optimization was 1.19 μm.
Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased ...perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models.
Aim of the study: Astrogliosis is a key contributor for many neurological disorders involving apoptosis, neuroinflammation and subsequent neuronal death. Silibinin, a polyphenol isolated from milk ...thistle (Silybum marianum), has been shown to suppress the astrocyte activation in various neurodegenerative disorders and also exhibit a neuroprotective role in neuroinflammation-driven oxidative damage. The present study was designed with an aim to investigate the neuroprotective effects of Silibinin against LPS induced oxido-inflammatory cascade and astrocyte activation.
Materials and methods: We have used in-silico molecular modelling techniques to study the interaction and binding affinity of silibinin with chemokine receptors associated with neuroinflammation. We have also tested silibinin against LPS induced oxido-inflammatory cascade and astrocyte activation in C6 glia cell lines.
Results: In the present study, we found that treatment with silibinin significantly attenuates LPS-oxidative-nitrosative stress in C6 astrocytoma cells. We also observed the significant inhibition of induced astrocyte activity after treatment with silibinin. Moreover, molecular modelling studies have proposed a binding pose of silibinin with binding sites of p38 MAPK, CX3CR1 and P2X4 which is an important downstream cascade involved in glia cell activation and neuroinflammation.
Conclusions: Overall, the findings from the current study suggests that silibinin exhibits neuroprotective activity by attenuating oxidative damage and astrocytes activation.
•Novel approach to fabricate Nafion with PVA into nanofiber mats by electrospinning.•Membrane possessed metal ion adsorption properties and strong mechanical properties.•Adsorption capacity for metal ...ions (mgg−1) Cu2+ 59.1, Cr3+ 42.5, Co2+ 24.7, As3+ 22.7.•Scope for other applications by engineering surface area and exposing ionic properties.
A novel approach to fabricate Nafion PFSA resin with polymer polyvinyl alcohol (PVA) into nanofiber membrane, as a functional adsorbent for heavy metal ions had been achieved. Nafion PFSA in N,N-dimethylacetamide (DMAc) was blended with aqueous solution of PVA by sol gel method and fabricated by electrospinning. Electrospun Nafion-PVA mats had exhibited more stability, better mechanical and adsorption properties. These nanofiber membranes possessed adsorption properties based on observed adsorption capacity for Cu (II) 59.1mgg−1, Cr (III) 42.5mgg−1, Co (II) 24.7mgg−1, and As (III) 22.7mgg−1 metal ions in an aqueous solution. These nanofiber membranes were characterized by Scanning Electron Microscopy (SEM), High-Resolution Transmission Electron Microscopy (HRTEM) images, Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) for their morphological, structural and adsorption properties.
Electrospinning method was optimized for fabrication of functional and mechanically strong nanofiber mats by reinforcement of Nafion resin into polymer polyacrylonitrile (PAN). Nafion and PAN were ...blended by the sol-gel method using the solvents N,N-dimethylacetamide (DMAc) and N,N-dimethylformamide (DMF). Fabricated membranes were characterized by their geometrical, physicochemical and mechanical properties. The study revealed that, nanofibers of 450-80 nm diameter were fabricated. The Nafion-PAN composite nanofiber exhibited more stability and better mechanical properties due to reinforcement of Nafion into PAN matrices. These composite nanofiber mats exhibited the ultimate tensile strength (UTS) of 376 and 286 kPa as compared to UTS of 157 kPa for neat PAN nanofibers. Nanofiber membranes also possessed adsorption properties showing an adsorption removal affinity for Cr super(3+) metal ions up to approximately 45%. Electrospun nanofiber mats also exhibited high surface area and can have multifaceted applications.
Higher demand for refrigeration systems adversely affects power grids, causing blackouts, increased electricity expenses, and carbon emissions. However, using renewable energy thermally driven ...refrigeration absorption systems is a promising alternative. The higher initial cost and dependency on local weather, large space area for installation, and complex design are hurdles for the widespread of these systems compared to the conventional vapor compression refrigeration systems. This study uses response surface methodology to model a real vapor absorption machine (VAM) incorporated with the measured data. This VAM is the refrigeration machine of a solar-powered absorption cooling system (SPACS) integrated with thermal energy storage for milk chilling installed and operated in Jaipur (India). The weather data for 2022 is used in performance and productivity analysis in this study. The results show that most of the summer months, the system can produce desired cooling to take down the 1000 l of milk within 3 h as per standards ISO 5708 – 2 II. Moreover, the solar loop still has sufficient driving heat to charge the 1000 l cold storage tank/another milk tank. During winter days, it takes a significant system response time (SRT) to reach the thermal energy storage (TES) up to 95 °C; after that, VAM operates significantly underperforming due to weak solar insolation; even the system cannot provide cooling to the first milking. The system produces monthly cooling energy of up to 2356 kWh in summer and is reduced to 620 kWh during monsoon and winter. The maximum value of the coefficient of performance (COPVAM) is achieved as 0.55 with an average of 0.41–0.46 during summer months whereas 0.34–0.39 during monsoon and winter days. The system's energy efficiency ratio (EER) ranges from 2.5 to 6.5, with an overall average of 4.55, which is far better than the conventional vapor compression refrigeration systems. The LCOE for the SPACS has been estimated to be 0.177 $/kWh, whereas the simple payback period is 12.4 years, and the discounted payback period is 19.7 years.
Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have ...been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear patterns and significant variations in the randomness of characteristics across various categories of real-world time series data, achieving effectiveness and robustness simultaneously poses a considerable challenge for specific deep-learning models. We have proposed a novel prediction framework with a multi-phase feature selection technique, a long short-term memory-based autoencoder, and a temporal convolution-based autoencoder to fill this gap. The multi-phase feature selection is applied to retrieve the optimal feature selection and optimal lag window length for different features. Moreover, the customized stacked autoencoder strategy is employed in the model. The first autoencoder is used to resolve the random weight initialization problem. Additionally, the second autoencoder models the temporal relation between non-linear correlated features with convolution networks and recurrent neural networks.
Finally, the model's ability to generalize, predict accurately, and perform effectively is validated through experimentation with three distinct real-world time series datasets. In this study, we conducted experiments on three real-world datasets: Energy Appliances, Beijing PM2.5 Concentration, and Solar Radiation. The Energy Appliances dataset consists of 29 attributes with a training size of 15,464 instances and a testing size of 4239 instances. For the Beijing PM2.5 Concentration dataset, there are 18 attributes, with 34,952 instances in the training set and 8760 instances in the testing set. The Solar Radiation dataset comprises 11 attributes, with 22,857 instances in the training set and 9797 instances in the testing set. The experimental setup involved evaluating the performance of forecasting models using two distinct error measures: root mean square error and mean absolute error. To ensure robust evaluation, the errors were calculated at the identical scale of the data. The results of the experiments demonstrate the superiority of the proposed model compared to existing models, as evidenced by significant advantages in various metrics such as mean squared error and mean absolute error. For PM2.5 air quality data, the proposed model's mean absolute error is 7.51 over 12.45, about ∼40% improvement. Similarly, the mean square error for the dataset is improved from 23.75 to 11.62, which is ∼51%of improvement. For the solar radiation dataset, the proposed model resulted in ∼34.7% improvement in means squared error and ∼75% in mean absolute error. The recommended framework demonstrates outstanding capabilities in generalization and outperforms datasets spanning multiple indigenous domains.
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