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.     

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.     

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.     

SFFS–SVM based prostate carcinoma diagnosis in DCE-MRI via ACM segmentation

The prostate carcinoma is amongst the most commonly occurring cancers in Taiwanese males. Moreover, it is one of the chief reasons for cancer deaths among Taiwanese men, and early diagnosis of prostate cancer is vital for effective treatment. In this work, a diagnosis model for identifying the prostate carcinoma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. The urologists utilize the DCE-MRI as a support mechanism for better diagnosis of the carcinoma development in the prostate. Gadolinium is utilized as the contrast agent for the DCE-MRI data, and it was injected once and the time series data were captured at distinct time intervals of 0, 20, 60, and 100 s correspondingly. Primarily, after pre-processing the DCE-MRI information, the prostate data is segmented by employing the active contour model. Subsequently, 136 features are extracted from the segmented prostrate expanse of the DCE-MRI data, and the relative intensity change curve is computed. Afterward, Fisher’s discriminant ratio and sequential forward floating selection is deployed for choosing ten highly discriminative features. Lastly, the segmented prostate regions are classified into two groups, namely: tumor and normal classes by employing the support vector machine classifier. The experimental results elucidate that the proposed system is superior on the subject of accuracy, sensitivity, and specificity when compared with specific existing methods. Additionally, the proposed system also demonstrates a 94.75% accuracy. Moreover, this signifies the fact that the proposed method for analyzing the DCE data has shown prodigious prospects in the prostate carcinoma diagnosis.

     

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.     

Machine Learning and Deep Learning powered satellite communications: Enabling technologies,applications, open challenges,and future research directions

The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government-funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard-to-reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.     

Strength design of composite beam using gradient and particle swarm optimization

This work addresses the optimum design of a composite box-beam structure subject to strength constraints. Such box-beams are used as the main load carrying members of helicopter rotor blades. A computationally efficient analytical model for box-beam is used. Optimal ply orientation angles are sought which maximize the failure margins with respect to the applied loading. The Tsai–Wu–Hahn failure criterion is used to calculate the reserve factor for each wall and ply and the minimum reserve factor is maximized. Ply angles are used as design variables and various cases of initial starting design and loadings are investigated. Both gradient-based and particle swarm optimization (PSO) methods are used. It is found that the optimization approach leads to the design of a box-beam with greatly improved reserve factors which can be useful for helicopter rotor structures. While the PSO yields globally best designs, the gradient-based method can also be used with appropriate starting designs to obtain useful designs efficiently.     

Preparation and pool boiling characteristics of copper nanofluids over a flat plate heater

The copper nanoparticles of average size of 10 nm have been prepared by the sputtering method and characterized through atomic force microscopy (AFM), X-ray diffraction (XRD) and transmission electron microscopy (TEM). The pool boiling heat transfer characteristics of 0.25%, 0.5% and 1.0% by weight concentrations of copper nanoparticles has been studied. Different copper based nanofluids were prepared in both, distilled water and distilled water with 9.0 wt% of sodium lauryl sulphate anionic surfactant (SDS). The pool boiling heat transfer data were acquired for the boiling of nanofluids over a 30 mm square and 0.44 mm thick stainless steel plate heater. The experimental results show that for the critical heat flux of pure water is 80% higher than that of water–surfactant fluid. Also, it was found that the critical heat flux for 0.25%, 0.5% and 1.0% concentrations of copper nanoparticles in copper–water nanofluids are 25%, 40% and 48% higher than that of pure water. But in the case of copper–water with surfactant nanofluids comparing with pure water, the CHF decreases to 75%, 68%, and 62% for respective concentrations of copper nanoparticles. The heat transfer coefficient decreases with increase of nanoparticles concentration in both water–copper and water–copper with surfactant nanofluids.     

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...