Evolution of supramolecular healable composites: a minireview

Efforts to further extend the range of applications of polymer based materials have resulted in the recent production of healable polymers that can regain their strength after damage. Within this field of healable materials, supramolecular polymers have been subject to extensive investigation. By virtue of their reversible non‐covalent interactions, cracks and fractures in such polymers can be readily and repeatedly healed in order to regain key physical properties. However, many supramolecular polymers are relatively weak and elastomeric in nature, which renders them unsuitable for high strength structural applications. To overcome these deficiencies, preliminary studies have shown that it is possible to reinforce supramolecular polymers with microscale and nanoscale fillers to afford composites that are not only stronger and stiffer compared with the polymers alone but also retain their healing abilities. In this minireview we discuss the evolution of these supramolecular composites and their advantages over more conventional, covalent polymeric materials. © 2014 Society of Chemical Industry     

Mitigation of Major Peanut Allergens by Pulsed Ultraviolet Light

Peanut allergy represents one of the most severe IgE-mediated reactions with food, but to date, the only effective way to prevent peanut allergy is total avoidance. If allergens could be mitigated during food processing before a product reaches the consumer, this would substantially lessen the food allergy problem. The efficacy of pulsed ultraviolet light (PUV), a novel food processing technology, on reducing peanut allergens, was examined. This study revealed for the first time that PUV was also capable of deactivating Ara h 2, the most potent allergenic protein of peanut. Protein extracts from raw and roasted peanuts were treated for 2, 4, and 6?min and peanut butter slurry was treated for 1, 2, and 3?min in a Xenon Steripulse XL 3000? PUV system. The distance from the central axis of the lamp was varied at 10.8, 14.6, and 18.2?cm. The SDS?CPAGE showed a reduction in the protein band intensity for Ara h 1, Ara h 2, and Ara h 3 at the energy levels ranging from 111.6 to 223.2?J/cm². Reduction of the protein band intensity for peanut allergens increased with treatment time but decreased with increased distance from the PUV lamp. The ELISA for peanut extracts and peanut butter slurry showed a reduction in IgE binding of up to 12.9- and 6.7-folds, respectively, compared to control.     

Organometallic L-Alanine Cadmium Iodide Crystals for Optical Device Fabrication

Single crystals of L-alanine cadmium iodide (LACI) were grown by the slow evaporation technique at room temperature. A single-crystal X-ray diffraction (SXRD) model was used to evaluate the crystal structure of the as-grown LACI crystal. The energy dispersive X-ray (EDX) analysis and ultraviolet-visible-near infrared (UV-vis-NIR) transmittance studies were carried out, and the results reveal the presence of elements in the title compound. From the transmittance data, the optical bandgap as a function of photon energy was estimated, and the different optical constants were calculated. A fluorescence study was performed for the LACI crystal. Thermogravimetric and differential thermal analyses have also been studied to investigate the thermal property of the LACI crystal. The efficiency of the second harmonic generation (SHG) of the title crystal was investigated. The magnetic and electrical properties were estimated by the vibrating sample magnetometer (VSM) analysis and impedance study, respectively.     

Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

Applied Intelligence - With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, an intrusion...     

Microscopic and Spectroscopic Evaluation of Inactivation of <Emphasis Type="Italic">Staphylococcus aureus</Emphasis> by Pulsed UV Light and Infrared Heating

Pulsed UV light and infrared heat-treated Staphylococcus aureus cells were analyzed using transmission electron microscopy to identify the cell damage due to the treatment process. A 5-s treatment with pulsed UV light resulted in complete inactivation of S. aureus even after enrichment. The temperature increase during the pulsed UV light treatment was insignificant, which suggested a nonthermal treatment. S. aureus was also infrared heat treated using an infrared heating system with six infrared lamps. Five milliliters of S. aureus cells in phosphate buffer was treated at 700°C lamp temperature for 20 min. The microscopic observation clearly indicated that there was cell wall damage, cytoplasmic membrane shrinkage, cellular content leakage, and mesosome disintegration after both pulsed UV light and infrared treatments. Fourier transform infrared microspectrometry was successfully used to classify the pulsed UV light and infrared heat-treated S. aureus by discriminant analysis.     

A novel method for determination of a time period for stabilization of power generation of microbial fuel cell with effect of microorganisms

Microbial fuel cells (MFCs) are quickly gaining traction in the mainstream industry due to their capabilities in simultaneous power generation and wastewater purification. They use bacteria like Shewanella and Geobacter as primary units for the same. However, their power generation capabilities are limited by a lack of stability in certain configurations. For the development of appropriate power storage and management systems, this instability must be investigated. Therefore, the present study proposes the artificial intelligence (AI) methodology of artificial neural search (ANS) networks to predict the period for stabilization of power generation of microbial fuel cell in the presence of microorganisms. An output voltage has been measured as a function of time (approximately 1600 h). A stabilization period of power generation has been predicted from the slope obtained from the graph of voltage vs time. The analysis of the ANS model indicated that the power generation stabilization has occurred between 12th and 16th weeks. Experiments were then performed to validate the findings from the ANS model. This may serve as an indication for the development of energy management and storage systems that can account for the trends observed during this study     

Fuzzy-Based Cluster Head Amendment (FCHA) Approach to Prolong the Lifetime of Sensor Networks

Wireless Personal Communications - Wireless sensor networks (WSN) consist of large number of sensor nodes that work collaboratively. Sensors segregate groups with similar traits and get arranged in...     

Fabrication of Biogenic Silica Nanostructures from Sorghum bicolor Leaves for Food Industry Applications

Silicon - Due to the large production of sorghum, the generation of associated agricultural residues, which contain high contents of silica, is inevitable. Also, these agricultural residues are not...     

An Efficient Sound and Data Steganography Based Secure Authentication System

The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks. Further, the pin-based security system is an inadequate mechanism for handling such a scenario. The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data. This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound, thereby disregarding the pins’ manual verification. Further, the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches. Firstly, a random signal is encrypted, and then it is transformed into a wave file, after which it gets transmitted in a short burst via the device’s speakers. Subsequently, the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for pairing. Besides, this model requires two devices/gadgets with speakers and a microphone, and no extra hardware such as a camera, for reading the QR code is required. The first module is tested with real-time data and generates high scores for the widely accepted accuracy metrics, including precision, Recall, F1 score, entropy, and mutual information (MI). Additionally, this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other files. This steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a file. Several encryption algorithms and their combinations are taken for this system to compare the resultant file size. Both these systems engender high accuracies and provide secure connectivity, leading to a sustainable communication ecosystem.     

Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems, preventing them from providing high quality recommendations. Inspired by the great success of deep learning in a wide range of fields, this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations. The proposed deep learning model is designed as a hybrid architecture with three key networks, namely autoencoder (AE), multilayered perceptron (MLP), and generalized matrix factorization (GMF). The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel. Next, MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features. Finally, the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks. We conducted extensive experiments on two benchmark datasets, MoiveLens100K and MovieLens1M, using four standard evaluation metrics. Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance. Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.