Syngas Production via Bi-Reforming of Methane Over Fibrous KCC-1 Stabilized Ni Catalyst

Topics in Catalysis - Bi-reforming of methane (BRM) technology has the potential to serve as an alternative energy source while also mitigating greenhouse gas emissions. However, the main hurdle in...     

A moderated mediation model linking entrepreneurial leadership to green innovation: An upper echelons theory perspective

Despite the dominant discourses on the important role of entrepreneurial leadership in firms' development, related theoretical and empirical studies are still lacking in the environmental management area. Adopting upper echelons theory, this study develops a moderated mediation model that examines how entrepreneurial leadership affects the adoption of green innovation (GI) through organizational learning culture and how environmental dynamism moderates the mediating effect. This theoretical model is tested using a sample of 248 Chinese firms. Findings support the positive impact of entrepreneurial leadership on GI and the mediating role of organizational learning culture. The moderated mediation path analysis shows that environmental dynamism moderates the indirect effect of entrepreneurial leadership on GI via organizational learning culture. Overall, this study adds to entrepreneurship and environmental management scholarship by providing novel insights into the key role of entrepreneurial leadership in embracing GI.     

Frictional material selection of dry clutch disc based on the weighted factor and finite element methods

The main task of the frictional facing of a clutch disc is to transfer the power from the driving to the driven parts. High frictional heat will be generated when the frictional facing comes into contact with the moving surfaces (flywheel and pressure plate). In this research paper, the selection of the frictional-facing material of the clutch disc has been investigated to obtain the optimal thermal behavior of the clutch system. It used five different frictional materials (St37-2, HCC, G95, LUK, and Tiger), and then applied the Weighted Factor Method to find the optimal frictional material that can be used in the clutch disc. In the second part, an axisymmetric finite element (FE) model has been developed to simulate the working of the clutch system during the heating phase. A comparison was made between the results of the thermal problem using the FE method and the Weighted Factor Method, where a high agreement was obtained between them to determine the optimal frictional material for the clutch disc. It was found that the frictional material (HCC) is the superior one with the highest performance index equal to 90.7 and the lowest values of the maximum temperature (353.7 K). In contrast, the frictional material (St37-2) is the poorest one, with the lowest performance index equal to 30.7 and the highest values of the maximum temperature (378.2 K).     

De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images. The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise. The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section. The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images. Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images. The proposed MCR de-noising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise. In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.     

Impact of salicylic acid and calcium chloride on quality attributes of peach stored at refrigeration temperature

Food Science and Biotechnology - Salicylic acid (SA) in different concentration were used to assess its individual effect as well as combined impact with 3% calcium chloride (CaCl2) on Swat No. 8...     

Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms—Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.