Effect of the lactoperoxidase system against three major causal agents of disease in mangoes

The antibacterial activity of the lactoperoxidase system (LPS) on the growth of Xanthomonas campestris, the causal agent of bacterial black spot in mangoes, Botryodiplodia theobromae, the causal agent of stem-end rot disease in mangoes, and Colletotrichum gloeosporioides, the causal agent of anthracnose disease in mangoes, was determined during culture at 30 degrees C and at several pH values (4.5, 5.5, and 6.5). When the results of using the LPS were compared with those from control cultures without the LPS reagents, the growth of the three microorganisms was totally inhibited in all of the conditions tested. Viability tests enumerating cultivable cells of X. campestris showed that the LPS had a bactericidal effect, whatever the pH value. This effect is faster at pH 5.5, corroborating the results reported in the literature (optimal pH for the LPS efficiency). Further, we proved that hydrogen peroxide alone had little inhibition effect on the growth of the microorganisms studied. This compound is essentially used to convert thiocyanate into hypothiocyanate during the lactoperoxidase reaction. The potential of the LPS for the postharvest treatment of the fruits for controlling microbial diseases was thus demonstrated. Nevertheless, further studies are needed on fresh fruits before envisaging any application.     

Focal region fields of dual reflector antennas

A simplified analytical method to predict the field distribution in the focal region of dual reflector antennas having circular symmetry is presented. The method is used to study the field distribution at the subdish and in the focal plane, and hence the ability of the antenna in focusing the incident wave. Experimental results obtained on a 2.6-m spherical Gregorian reflector operating at 9.0 GHz agree well with theoretical ones.     

A new approach for development of quadruped robot based on biological concepts

This paper proposes the design and development results of a new quadruped robot. The proposed new quadruped robot has a couple of advantages of flexible locomotion. The quadruped robot is designed and modeled based on a new concept that is the structure model with three segments of quadruped legs. New leg configuration with the simplified operation of four hip actuators is introduced. The posture of the new quadruped robot is more similar to the posture of dog than that of the previous quadruped robots. The objective of this paper is to develop a quadruped robot, which can walk and run in a trot gait with a simple PID controller. Numerical simulation and experimental results are shown to prove the locomotion performance of the proposed controller for the proposed quadruped robot.     

Context-aware wireless sensor networks for assisted living and residential monitoring

Improving the quality of healthcare and the prospects of "aging in place" using wireless sensor technology requires solving difficult problems in scale, energy management, data access, security, and privacy. We present AlarmNet, a novel system for assisted living and residential monitoring that uses a two-way flow of data and analysis between the front- and back-ends to enable context-aware protocols that are tailored to residents' individual patterns of living. AlarmNet integrates environmental, physiological, and activity sensors in a scalable heterogeneous architecture. The SenQ query protocol provides real-time access to data and lightweight in-network processing. Circadian activity rhythm analysis learns resident activity patterns and feeds them back into the network to aid context-aware power management and dynamic privacy policies.     

Poly(methyl methacrylate)-functionalized reduced graphene oxide-based core–shell structured beads for thermally conductive epoxy composites

Thermally conductive epoxy nanocomposite with core–shell structured filler beads has been prepared. The core represents plasma-treated poly(methyl methacrylate) bead, and the shell, amine-functionalized reduced graphene oxide (frGO) sheets. The negatively charged core and the positively charged shell form core–shell unified structure through electrostatic attraction and the conductive bridges are formed among neighboring filler particles in the composite mass. The epoxy composite prepared with these core–shell structured filler shows a thermal conductivity of 0.72 W m⁻¹ K⁻¹ for an overall frGO concentration of as low as 0.96 wt %. Pal model has been applied to evaluate the effective thermal conductivity of frGO sheets that have been realized in the epoxy composition. Assuming the maximum possible volume packing fraction of the spherical beads for random jamming to be equal to 0.63, the effective thermal conductivity has been estimated as 280 W m⁻¹ K⁻¹. Evaluation of the effective thermal conductivity is a step forward to in-depth study of real contribution of the highly conductive fillers in the polymer composites. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47377.     

Formation and Degradation of Beta-casomorphins in Dairy Processing

Milk proteins including casein are sources of peptides with bioactivity. One of these peptides is beta-casomorphin (BCM) which belongs to a group of opioid peptides formed from β-casein variants. Beta-casomorphin 7 (BCM7) has been demonstrated to be enzymatically released from the A1 or B β-casein variant. Epidemiological evidence suggests the peptide BCM 7 is a risk factor for development of human diseases, including increased risk of type 1 diabetes and cardiovascular diseases but this has not been thoroughly substantiated by research studies. High performance liquid chromatography coupled to UV-Vis and mass spectrometry detection as well as enzyme–linked immunosorbent assay (ELISA) has been used to analyze BCMs in dairy products. BCMs have been detected in raw cow's milk and human milk and a variety of commercial cheeses, but their presence has yet to be confirmed in commercial yoghurts. The finding that BCMs are present in cheese suggests they could also form in yoghurt, but be degraded during yoghurt processing. Whether BCMs do form in yoghurt and the amount of BCM forming or degrading at different processing steps needs further investigation and possibly will depend on the heat treatment and fermentation process used, but it remains an intriguing unknown.     

Two‐Terminal Multibit Optical Memory via van der Waals Heterostructure

2D van der Waals (vdWs) heterostructures exhibit intriguing optoelectronic properties in photodetectors, solar cells, and light‐emitting diodes. In addition, these materials have the potential to be further extended to optical memories with promising broadband applications for image sensing, logic gates, and synaptic devices for neuromorphic computing. In particular, high programming voltage, high off‐power consumption, and circuital complexity in integration are primary concerns in the development of three‐terminal optical memory devices. This study describes a multilevel nonvolatile optical memory device with a two‐terminal floating‐gate field‐effect transistor with a MoS₂/hexagonal boron nitride/graphene heterostructure. The device exhibits an extremely low off‐current of ≈10^?14 A and high optical switching on/off current ratio of over ≈10⁶, allowing 18 distinct current levels corresponding to more than four‐bit information storage. Furthermore, it demonstrates an extended endurance of over ≈10⁴ program–erase cycles and a long retention time exceeding 3.6 × 10⁴ s with a low programming voltage of ?10 V. This device paves the way for miniaturization and high‐density integration of future optical memories with vdWs heterostructures.     

A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam

Horizontal displacement of hydropower dams is a typical nonlinear time-varying behavior that is difficult to forecast with high accuracy. This paper proposes a novel hybrid artificial intelligent approach, namely swarm optimized neural fuzzy inference system (SONFIS), for modeling and forecasting of the horizontal displacement of hydropower dams. In the proposed model, neural fuzzy inference system is used to create a regression model whereas Particle swarm optimization is employed to search the best parameters for the model. In this work, time series monitoring data (horizontal displacement, air temperature, upstream reservoir water level, and dam aging) measured for 11 years (1999–2010) of the Hoa Binh hydropower dam were selected as a case study. The data were then split into a ratio of 70:30 for developing and validating the hybrid model. The performance of the resulting model was assessed using RMSE, MAE, and R ². Experimental results show that the proposed SONFIS model performed well on both the training and validation datasets. The results were then compared with those derived from current state-of-the-art benchmark methods using the same data, such as support vector regression, multilayer perceptron neural networks, Gaussian processes, and Random forests. In addition, results from a Different evolution-based neural fuzzy model are included. Since the performance of the SONFIS model outperforms these benchmark models with the monitoring data at hand, the proposed model, therefore, is a promising tool for modeling horizontal displacement of hydropower dams.

     

An efficient in‐network caching decision algorithm for Internet of things

Recently, content‐centric networking (CCN) has become one of the important technologies for enabling the future networks. Along with its recognized potentialities as a content retrieval and dissemination solution, CCN has been also recently considered as a promising architecture for the Internet of things (IoT), because of 2 main features such as named‐based routing and in‐network caching. However, IoT is characterized by challenging features: small storage capacity of resource‐constrained devices due to cost and limitation of energy and especially transient data that impose stringent requirements on the information freshness. As a consequence, the intrinsic caching mechanisms existing in CCN approach do not well suit IoT domains; hence, providing a specific caching policy at intermediate nodes is a very challenging task. This paper proposes an effective multiattribute in‐network caching decision algorithm that performs a caching strategy in CCN‐IoT network by considering a set of crucial attributes including the content store size, hop count, particularly key temporal properties like data freshness, and the node energy level. Simulation results proved that our proposed approach outperforms 2 cache management schemes (probabilistic least recently used and AlwaysCache–first in first out in terms of improving total hit rate, reducing data retrieval delay, and enhancing content reusability in IoT environment).