Antifungal activity of five different essential oils in vapour phase for the control of Colletotrichum gloeosporioides and Lasiodiplodia theobromae in vitro and on mango

Mango fruit has high commercial value; however, major postharvest losses are encountered throughout the supply chain due to postharvest diseases. These results lead to the search for natural fungicide for postharvest diseases control. The antifungal effects of five essential oils (thyme, clove, cinnamon, anise and vitex) were assessed by disc volatilisation method. Thyme oil vapours at 5 μL per Petriplate, and clove and cinnamon oil at 8 μL per Petriplate showed 100% growth inhibition of mango pathogens in vitro. GC/MS analysis of essential oil showed thymol (23.88), o‐cymol (23.88) and terpinolene (23.88) as the major constituents of thyme oil. Clove and cinnamon oils contain 3‐allyl‐2‐methoxyphenol (37.42%) and benzofuran 3‐methyl (17.97%), respectively. Thyme oil as a fumigant at 66.7 μL L^?1 showed a significant (P < 0.05) inhibition on postharvest pathogens of mango fruits stored at 25 °C for 6 days. Results of our study suggest the possibility of using thyme oil as an alternate natural fungicide to manage postharvest diseases in mango.     

Information technology adoption and continuance: A longitudinal study of individuals’ behavioral intentions

Extant research on information technology (IT) adoption and continuance has not adequately modeled the times of adoption by individuals. This study argues that individuals adopt an innovation at different times and are likely to be influenced by different factors over time. The theoretical models are empirically validated using data gathered at three points in time through surveys of 132 users of a new innovation. The results indicate that the innovation attributes and individual characteristics influence individuals’ intentions to adopt the innovation during the early stage, and the innovation attributes and contextual factors impact the individuals’ intentions during the later stage.     

Adhesion energy can regulate vesicle fusion and stabilize partially fused states

Release of neurotransmitters from nerve terminals occurs by fusion of synaptic vesicles with the plasma membrane, and this process is highly regulated. Although major molecular components that control docking and fusion of vesicles to the synaptic membrane have been identified, the detailed mechanics of this process is not yet understood. We have developed a mathematical model that predicts how adhesion forces imposed by docking and fusion molecular machinery would affect the fusion process. We have computed the membrane stress that is produced by adhesion-driven vesicle bending and find that it is compressive. Further, our computations of the membrane curvature predict that strong adhesion can create a metastable state with a partially opened pore that would correspond to the ‘kiss and run’ release mode. Our model predicts that the larger the vesicle size, the more likely the metastable state with a transiently opened pore. These results contribute to understanding the mechanics of the fusion process, including possible clamping of the fusion by increasing molecular adhesion, and a balance between ‘kiss and run’ and full collapse fusion modes.     

Integration of textual cues for fine-grained image captioning using deep CNN and LSTM

Neural Computing and Applications - The automatic narration of a natural scene is an important trait in artificial intelligence that unites computer vision and natural language processing. Caption...     

CTAB-caped Cu nanoparticles doped on zeolitic imidazolate framework-ZIF-67 as bifunctional catalysts for oxygen-reduction and evolution reactions in alkaline media

Journal of Porous Materials - In order to facilitate the large-scale applications of rechargeable Zn–air batteries, non-noble-metal based materials with high activity for oxygen reduction...     

Data Mining for Vehicle Telemetry

This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.     

Secure Key Agreement with Rekeying Using FLSO Routing Protocol in Wireless Sensor Network

Wireless Personal Communications - A challenge in wireless sensor network (WSN) is to design an energy efficient and secure network protocol because sensors are the wireless device with limited...