The current research was commenced by reaction of 1,4-benzodioxane-6-amine (1) with 4-nitrobenzenesulfonyl chloride (2) in the presence of aqueous base under dynamic pH control at 9 to yield N-(2,3-dihydro-1,4-benzodioxin-6-yl)-4-nitrobenzenesulfonamide (3) which was further reacted with a series of alkyl/aralkyl halides (4a-i) in polar aprotic solvent using catalytic amount of lithium hydride which acts as base to afford some new N-alkyl/aralkyl-N-(2,3-dihydro-1,4-benzodioxin-6-yl)-4-nitrobenzenesulfonamides (5a-i). The projected structures of all the synthesized derivatives were characterized by contemporary techniques i.e., IR, 1H-NMR and EIMS. The biofilm Inhibitory action of all synthesized molecules was carried out against Escherichia coli and Bacillus subtilis. It was inferred from their results that 5f and 5e exhibited suitable inhibitory action against the biofilms of these bacterial strains. Moreover, their cytotoxicity was also checked and it was concluded that these synthesized molecules displayed docile cytotoxicity.
With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.
Drucker's knowledge-worker productivity theory and knowledge-based view of the firm theory are widely employed in many disciplines but there is little application of these theories in knowledge-based innovation among academic researchers. Therefore, this study intends to evaluate the effects of the knowledge management process on knowledge-based innovation alongside with mediating role of Malaysian academic researchers' productivity during the Pandemic of COVID-19. Using a random sampling technique, data was collected from 382 academic researchers. Questionnaires were self-administered and data was analyzed via Smart PLS-SEM. Knowledge management process and knowledge workers' productivity have a positive and significant relationship with the knowledge-based innovation among academic researchers during the Pandemic of COVID-19. In addition, knowledge workers' productivity mediates the relationship between the knowledge management process (knowledge creation, knowledge acquisition, knowledge sharing, and knowledge utilization) and knowledge-based innovation during the Pandemic of COVID-19. Results have also directed knowledge sharing as the key factor in knowledge-based innovation and a stimulating task for management discipline around the world during the Pandemic of COVID-19. This study provides interesting insights on Malaysian academic researchers' productivity by evaluating the effects of knowledge creation, acquisition, sharing, and application on the knowledge-based innovation among academic researchers during the Pandemic of COVID-19. These useful insights would enable policymakers to develop more influential educational strategies. By assimilating the literature of defined variables, the main contribution of this study is the evaluation of knowledge creation, acquisition, sharing, and utilization into knowledge-based innovation alongside the mediating role of knowledge workers productivity in the higher education sector of Malaysia during the Pandemic of COVID-19.
Campylobacter jejuni, gram-negative bacteria, is an infectious agent of foodborne disease-causing bloody diarrhea, abdominal pain, fever, Guillain-Barré syndrome (GBS) and Miller Fisher syndrome in humans. Campylobacter spp. with multidrug resistance to fluoroquinolones, tetracycline, and erythromycin are reported. Hence, an effective vaccine candidate would provide long-term immunity against C. jejuni infections. Thus, we used a subtractive proteomics pipeline to prioritize essential proteins, which impart a critical role in virulence, replication and survival. Five proteins, i.e. Single-stranded DNA-binding protein, UPF0324 membrane protein Cj0999c, DNA translocase FtsK, 50S ribosomal protein L22, and 50S ribosomal protein L1 were identified as virulent proteins and selected for vaccine designing. We reported that the multi-epitopes subunit vaccine based on CTL, HTL and B-cell epitopes combination possess strong antigenic properties and associates no allergenic reaction. Further investigation revealed that the vaccine interacts with the immune receptor (TLR-4) and triggered the release of primary and secondary immune factors. Moreover, the CAI and GC contents obtained through codon optimization were reported to be 0.93 and 53% that confirmed a high expression in the selected vector. The vaccine designed in this study needs further scientific consensus and will aid in managing C. jejuni infections.
Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.
In the aimed research study, a new series of N-(aryl)-3-[(4-phenyl-1-piperazinyl)methyl]benzamides was synthesized, which was envisaged as tyrosinase inhibitor. The structures of these newly designed molecules were verified by IR, 1H-NMR, 13C-NMR, EI-MS and CHN analysis data. These molecules were screened against tyrosinase and their inhibitory activity explored that these 3-substituted-benzamides exhibit good to excellent potential, comparative to the standard. The Kinetics mechanism was investigated through Lineweaver-Burk plots which depicted that molecules inhibited this enzyme in a competitive mode. Moreover, molecular docking was also performed to determine the binding interaction of all synthesized molecules (ligands) with the active site of tyrosinase enzyme and the results showed that most of the ligands exhibited efficient binding energy values. Therefore, it is anticipated that these molecules might serve as auspicious therapeutic scaffolds for treatment of the tyrosinase associated skin disorders.