Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks

With the development and wide adoption of industrial wireless sensor networks to support various domain applications, the boundary detection of continuous objects has become an important research challenge, where improving the accuracy of boundary area while reducing the energy consumption are the first-class citizens to be considered. To address this research challenge, this article proposes a two-stage boundary face detection mechanism, where sensor nodes are duty-cycled and to be deployed in a dense fashion. When the occurrence of potential events are recognized using the initially activated sensor nodes, the boundary faces of continuous objects are constructed through adopting planarization algorithms. Thereafter, sensor nodes contained in certain boundary faces are examined, where their sensory data are estimated using spatial interpolation methods. Certain sensor nodes are selected to be woken up, only when their sensory data suggest that they should be more appropriate candidates of boundary sensor nodes. Consequently, the size of boundary faces is reduced, and this coarse-to-fine refinement procedure iterates, until all sensor nodes contained in the boundary faces have been examined. Experimental evaluation result shows that the boundary area can be refined significantly and be more precise, where the half of the initial boundary face area should be reduced in most situations.     

New look on relay selection strategies for full-duplex multiple-relay NOMA over Nakagami-m fading channels

Wireless Networks - By removing the orthogonal use of radio-resources, non-orthogonal multiple access (NOMA) has been introduced to improve the spectral efficiency of fifth generation (5G) and...     

A comprehensive survey of AI-enabled phishing attacks detection techniques

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

     

Biomimetic Nanomaterial Strategies for Virus Targeting: Antiviral Therapies and Vaccines

The ongoing coronavirus disease 2019 (COVID-19) pandemic highlights the importance of developing effective virus targeting strategies to treat and prevent viral infections. Since virus particles are nanoscale entities, nanomaterial design strategies are ideally suited to create advanced materials that can interact with and mimic virus particles. In this progress report, the latest advances in biomimetic nanomaterials are critically discussed for combating viral infections, including in the areas of nanomaterial-enhanced viral replication inhibitors, biomimetic virus particle capture schemes, and nanoparticle vaccines. Particular focus is placed on nanomaterial design concepts and material innovations that can be readily developed to thwart future viral threats. Pertinent nanomaterial examples from the COVID-19 situation are also covered along with discussion of human clinical trial efforts underway that might lead to next-generation antiviral therapies and vaccines.     

A new denoising method for removing salt & pepper noise from image

Multimedia Tools and Applications - Digital image has a significant importance in many fields in human life such as, in medicine, photography, biology, astronomy, industry and defense. Thus, it...     

Usability evaluation of a virtual reality smartphone app for a living museum

This paper elaborates the empirical evidence of a usability evaluation of a VR and non-VR virtual tour application for a living museum. The System Usability Scale (SUS) was used in between participants experiments (Group 1: non-VR version and Group 2: VR version) with 40 participants. The results show that the mean scores of all components for the VR version are higher compared to the non-VR version, overall SUS score (72.10 vs 68.10), usability score (75.50 vs 71.70), and learnability (58.40 vs 57.00). Further analysis using a two-tailed independent t test showed no difference between the non-VR and VR versions. Additionally, no significant difference was observed between the groups in the context of gender, nationality, and prior experience (other VR tour applications) for overall SUS score, usability score, and learnability score. Α two-tailed independent t test indicated no significant difference in the usability score between participants with VR experience and no VR experience. However, a significant difference was found between participants with VR experience and no VR experience for both SUS score (t(38) = 2.17, p = 0.037) and learnability score (t(38) = 2.40, p = 0.021). The independent t test results indicated a significant difference between participant with and without previous visits to SCV for the usability score (t(38) = −2.31, p = 0.027), while there was no significant differences observed in other components. It can be concluded that both versions passed based on the SUS score. However, the sub-scale usability and learnability scores indicated some usability issue.

     

The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings

Field spectroscopy is a rapid and non-destructive analytical technique that may be used for assessing plant stress and disease. The objective of this study was to develop spectral indices for detection of Ganoderma disease in oil palm seedlings. The reflectance spectra of oil palm seedlings from three levels of Ganoderma disease severity were acquired using a spectroradiometer. Denoizing and data transformation using first derivative analysis was conducted on the original reflectance spectra. Then, comparative statistical analysis was used to select significant wavelength from transformed data. Wavelength pairs of spectral indices were selected using optimum index factor. The spectral indices were produced using the wavelength ratios and a modified simple ratio method. The relationship analysis between spectral indices and total leaf chlorophyll (TLC) was conducted using regression technique. The results suggested that six spectral indices are suitable for the early detection of Ganoderma disease in oil palm seedlings. Final results after regression with TLC showed that Ratio 3 is the best spectral index for the early detection of Ganoderma infection in oil palm seedlings. For future works, this can be used for the development of robust spectral indices for Ganoderma disease detection in young and mature oil palm using airborne hyperspectral imaging.     

Constructive Visual Analytics for Text Similarity Detection

Detecting similarity between texts is a frequently encountered text mining task. Because the measurement of similarity is typically composed of a number of metrics, and some measures are sensitive to subjective interpretation, a generic detector obtained using machine learning often has difficulties balancing the roles of different metrics according to the semantic context exhibited in a specific collection of texts. In order to facilitate human interaction in a visual analytics process for text similarity detection, we first map the problem of pairwise sequence comparison to that of image processing, allowing patterns of similarity to be visualized as a 2D pixelmap. We then devise a visual interface to enable users to construct and experiment with different detectors using primitive metrics, in a way similar to constructing an image processing pipeline. We deployed this new approach for the identification of commonplaces in 18th‐century literary and print culture. Domain experts were then able to make use of the prototype system to derive new scholarly discoveries and generate new hypotheses.     

Chemical investigation of oil of the seed of “White Todri” (Matthiola incana,R.Br.)

The fixed oil from the seeds of 舠White Todri舡,Matthiola incana, R.Br. (Cruciferae), of Indian origin, has been studied for its component acids. The fatty acid composition was found to be myristic (2.60%), palmitic (4.73%), steric (4.37%), arachidic (2.50%), lignoceric (?) (0.73%), oleic (32.17%), linoleic (21.70%), linolenic (10.70%), erucic (13.10%), and resin acids (7.40%).     

Alcoholysis of palm oil mid-fraction by lipase from Rhizopus rhizopodiformis

A mycelial lipase from Rhizopus rhizopodiformis was prepared in fragment form. The lipase was examined to catalyze the alcoholysis of palm oil mid-fraction (PMF) in organic solvents. High percentage conversions of PMF to alkyl esters were achieved when methanol or propanol was used as acyl acceptor. Of the two most prevalent fatty acids in PMF, palmitic acid seemed to be preferred over oleic acid in the formation of methyl and propyl esters. The optimal ratio of oil to methanol in the alcoholysis reaction is 1 to 2 moles. The lipase exhibited high alcoholysis activities in nonpolar solvents (log P>2), such as hexane, benzene, toluene, and heptane. The enzyme showed exceptionally high thermostability.