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.     

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).     

Growth of Bacillus cereus on Oil-blanched Potato Strips for "Home-style" French Fries

Fresh cut, oil blanched strips from whole potatoes stored at 7 or 13 °C were inoculated with approximately 3 or 5 log CFU/g Bacillus cereus and incubated at 21 or 26.7 °C for up to 9 h to model handling of "home-style" French fries. Whole potato storage at 13 °C and incubation at 26.7 °C resulted in faster growth than 7 or 21 °C. Frying (2 to 3.5 min at 185 °C) inactivated up to 5.1 log B. cereus spores. Oil blanched potato strips for "home-style" French fries should be stored at £ 21 °C or finish fried or discarded within 3 to 4 h.     

Growth and Production of Enterotoxin A by Staphylococcus aureus on "Home-style" French Fries

Staphylococcus aureus strains were inoculated onto fresh-cut oil-blanched potato strips stored at 21 or 26.7 °C for up to 9 h to determine if the microorganism was capable of growth and staphylococcal enterotoxin (SE) production. Potato strips were assayed for SE using a commercial ELISA kit prior to and following finish frying. S. aureus increased by 2.5 to 2.8 log CFU/g over 9 h at 26.7 °C, and SE was detected after 5 h. SE remained serologically detectable following finish frying of the potato strips. It is recommended that oil-blanched potato strips stored at 26.7 °C be finish fried and served, or discarded, within 3 to 4 h to prevent possible production of SE.     

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.     

Learning to Match the Schemas of Data Sources: A Multistrategy Approach

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are element location maps to address and price maps to listed-price. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.     

籼米中淀粉粒结合蛋白的分离鉴定及提取工艺优化

采用十二烷基硫酸钠（sodium dodecyl sulfate,SDS）缓冲体系分离提取碱洗籼米粉中的淀粉粒结合蛋白（starch granule-associated proteins,SGAPs）,经高效液相色谱-串联质谱法鉴定表明：碱洗得到上、中、下层籼米淀粉中的SGAPs均主要为淀粉合成酶I（granule-bound starch synthase I,GBSS I）,其他组分略有差别;其中以中层SGAPs含量最高,GBSS I达99.8%,谷蛋白质量分数仅为0.11%。采用响应面优化中层淀粉SGAPs的提取参数,得到最优条件为SDS添加量10.90%、煮沸时间4.10?min、MgSO4浓度0.90?mmol/L,此时SGAPs提取量为（1?028.61±0.05）μg/g。     

Assessment of SGS closure for isochoric combustion of hydrogen-air mixture

Direct numerical simulation (DNS) data of freely propagating turbulent premixed flame of stoichiometric hydrogen air mixture inside a closed vessel is analysed to study a sub-grid combustion closure based on unstrained flamelet approach. This modeling framework needs closures for the sub-grid scale (SGS) reaction rate and scalar dissipation rate. The results show that the closure models for these two SGS quantities work quite well. The dissipation rate closure involves a scale dependent parameter, ${\beta }_c$, which is related to the flame curvature induced effects. The reactivity of reactant mixture increases with time in isochoric combustion because the mixture temperature and pressure increase with time. This also influences the parameter ${\beta }_c$ and thus the dynamic evaluation of this parameter is investigated using the DNS data.