Effects of forage provision to dairy calves on growth performance and rumen fermentation: A meta-analysis and meta-regression

A meta-analysis of the potential effect of forage provision on growth performance and rumen fermentation of dairy calves was conducted using published data from the literature (1998–2016). Meta-regression was used to evaluate the effects of different forage levels, forage sources, forage offering methods, physical forms of starter, and grain sources on the heterogeneity of the results. We considered 27 studies that reported the effects of forage provision to dairy calves. Estimated effect sizes of forage were calculated on starter feed intake, average daily gain (ADG), feed efficiency (FE), body weight (BW), and rumen fermentation parameters. Intake of starter feed, ADG, BW, ruminal pH, and rumen molar proportion of acetate increased when supplementing forage but FE decreased. Heterogeneity (the amount of variation among studies) was significant for intake of starter feed, ADG, FE, final BW, and rumen fermentation parameters. Improving overall starter feed intake was greater in calves offered alfalfa hay compared with those offered other types of forages. During the milk feeding and overall periods, improving ADG was greater for calves fed a high level of forage (>10% in dry matter) compared with those fed a low level of forage (≤10% in dry matter) diets. The advantages reported in weight gain at a high level of forage could be due to increased gut fill. Improving overall ADG was lower for calves offered forages with textured starter feed compared with ground starter feed. The meta-regression analysis revealed that changes associated with forage provision affect FE differently for various forage sources and forage offering methods during the milk-feeding period. Forage sources also modulated the effect of feeding forage on ruminal pH during the milk-feeding period. In conclusion, forage has the potential to affect starter feed intake and performance of dairy calves, but its effects depend on source, level, and method of forage feeding and physical form of starter feed independently of grain sources included in the starter feed.     

Experimental investigation of hysteretic dynamic effect in capillary pressure–saturation relationship for two‐phase flow in porous media

Well‐defined laboratory experiments have been carried out to investigate hysteretic dynamic effect in capillary pressure–saturation relationships for two‐phase flow in homogeneous and heterogeneous (layered) porous media. Conceptually, the dependence of the capillary pressure curves on the rate of change of saturation (dS_w/dt) is defined as the dynamic effect in capillary pressure relationship, which is indicated by a dynamic coefficient, τ (Pa s) and it determines the rate at which two‐phase flow equilibrium is reached, i.e., dS_w/dt = 0 where S_w and t are the water saturation and time, respectively. The dependences of τ on various fluid and porous materials properties have been studied in the context of drainage; but, there is limited study for imbibition and the hysteresis of τ?S_w relationships. As such, the emphasis in this article is on reporting τ?S_w curves for imbibition while also demonstrating the hysteresis in the τ?S_w relationships by comparing τ?S_w curves for drainage (previously reported) and imbibition (this study) in carefully designed laboratory experiments. Homogeneous sand samples composed of either fine (small particle size and lower permeability) or coarse (larger particle size and higher permeability) sand have been used for these experiments. Furthermore, a layered domain made of a find sand layer sandwiched between two coarse sand layers is used as a model of heterogeneous domain. The results of the study confirm that the τ?S_w relationships are hysteretic in nature and, as such, the speed to flow equilibrium should vary depending on whether drainage or imbibition takes place. At a particular water saturation, the magnitudes of the dynamic coefficient (τ) are found to be generally higher for imbibition, which imply that the speed to flow equilibrium at the same saturation will be slower for imbibition. © 2013 The Authors. AIChE Journal, published by Wiley on behalf of the AIChE. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. AIChE J, 59: 3958–3974, 2013     

Implementation of meshless LBIE method to the 2D non‐linear SG problem

In this paper the meshless local boundary integral equation (LBIE) method for numerically solving the non‐linear two‐dimensional sine‐Gordon (SG) equation is developed. The method is based on the LBIE with moving least‐squares (MLS) approximation. For the MLS, nodal points spread over the analyzed domain are utilized to approximate the interior and boundary variables. The approximation functions are constructed entirely using a set of scattered nodes, and no element or connectivity of the nodes is needed for either the interpolation or the integration purposes. A time‐stepping method is employed to deal with the time derivative and a simple predictor–corrector scheme is performed to eliminate the non‐linearity. A brief discussion is outlined for numerical integrations in the proposed algorithm. Some examples involving line and ring solitons are demonstrated and the conservation of energy in undamped SG equation is investigated. The final numerical results confirm the ability of method to deal with the unsteady non‐linear problems in large domains. Copyright © 2009 John Wiley & Sons, Ltd.     

New wireless connection between user and VE using speech processing

This paper presents a novel speak-to-VR virtual-reality peripheral network (VRPN) server based on speech processing. The server uses a microphone array as a speech source and streams the results of the process through a Wi-Fi network. The proposed VRPN server provides a handy, portable and wireless human machine interface that can facilitate interaction in a variety interfaces and application domains including HMD- and CAVE-based virtual reality systems, flight and driving simulators and many others. The VRPN server is based on a speech processing software development kits and VRPN library in C++. Speak-to-VR VRPN works well even in the presence of background noise or the voices of other users in the vicinity. The speech processing algorithm is not sensitive to the user’s accent because it is trained while it is operating. Speech recognition parameters are trained by hidden Markov model in real time. The advantages and disadvantages of the speak-to-VR server are studied under different configurations. Then, the efficiency and the precision of the speak-to-VR server for a real application are validated via a formal user study with ten participants. Two experimental test setups are implemented on a CAVE system by using either Kinect Xbox or array microphone as input device. Each participant is asked to navigate in a virtual environment and manipulate an object. The experimental data analysis shows promising results and motivates additional research opportunities.     

Amygdalin Contents of Oil and Meal from Wild Almond: Effect of Different Heat Pretreatment and Extraction Methods

The effects of different heat treatment methods on the extraction yield of oil and the amygdalin contents of the wild almond meal and oil were investigated. When using hexane as a solvent for the extraction, oil yield and amygdalin contents of the extracted oils increased by increasing the applied temperature as the pretreatment (46.1–51.6%, w/w, for oil yield and 26–49 mg/100 mL oil for the amygdalin content). When using mechanical oil extraction, hot-press resulted in higher oil yield (23.2%) than did the cold-press (15.6%) but the amygdalin levels of the extracted oils were not significantly different (12.8–12.9 mg/100 mL oil). Autoclaving ground wild almond and hot-press resulted in a significant increase in the peroxide and acid values of the oils. Investigation of fatty acid profiles of different samples showed that heat treatment and extraction method in this study did not impact the fatty acid profiles of the extracted oils.     

Big Data Based Self-Optimization Networking in Next Generation Mobile Networks

Using self-optimization techniques is the only viable solution for increasing the efficiency in next generation mobile networks. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to microcell and femto-cell users, considering the limited resources in radio access networks. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process. Based on the meaningful extracted information, the SON decision maker will be able to adjust network parameters and resource allocation factors in a more intelligent manner. The experimental results show that despite the tremendous volume of the analyzed data—which is hundreds of times bigger than usual methods—it is possible to improve the KPIs, such as throughput, up to 30% by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25% when the number of parameters is too high. Moreover, numerical results indicate the superiority of using support vector machine learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15% increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.     

Realization of low-temperature and selective NO2 sensing of SnO2 nanowires via synergistic effects of Pt decoration and Bi2O3 branching

Metal oxide semiconductors with branched structures, such as branched nanowires (b-NWs), have promising properties for being used in gas sensors. In this work, we synthesized Pt-decorated Bi₂O₃-branched SnO₂ nanowires (NWs). NO₂ sensing studies revealed the superior capacity of a Pt-decorated Bi₂O₃-branched SnO₂ NWs gas sensor relative to pristine and branched SnO₂ gas sensors, and it worked at near room temperature (50 °C). The increased sensing capacity was related to the synergistic effects of Pt decoration and Bi₂O₃ branching, particularly the morphology of the gas sensor with branched structures, the promising effects of Pt as a noble metal with good catalytic activity, and the generation of homo- and heterojunctions in the Pt-decorated Bi₂O₃-branched SnO₂ NWs gas sensor. The results obtained in this work are useful for design and development of NO₂ gas sensors using a simple strategy, which can be easily extended to various metal oxides.     

800MHz～5GHZ单芯片RF IC的开发

美国加州大学洛杉矶分校(UCLA)和洛杉矶的个人风险投资企业WiLinx,联合开发出了可用1组RF电路接收不同高频信号的技术,并使用CMOS工艺生产出了应用该技术的接收电路芯片。用一个仅为3m m×3mm(2.9mm×2.4mm)的芯片就可以接收中心频率为800M H z￣5G H z、带宽为200kHz￣20MHz的     

Ambient temperature CO oxidation using copper manganese oxide catalysts prepared by coprecipitation: effect of ageing on catalyst performance

Copper manganese oxides are prepared using a coprecipitation procedure and studied for the oxidation of CO at ambient temperature. In particular, the effect of the ageing tune, i.e. the time that the precipitate remains in contact with the precipitating medium, is investigated. It is shown that this parameter is of crucial importance in controlling the catalytic performance and that catalysts which are aged for 30 min or 300 min give the best performance. Preliminary characterisation using powder X-ray diffraction indicates that a combination between CuO and copper manganese oxide may be responsible for the enhanced activity observed with these samples.     

An EM based probabilistic two-dimensional CCA with application to face recognition

Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction. The method instead of concatenating the columns of the images to the one-dimensional vectors, directly works with two-dimensional image matrices. Although 2DCCA works well in different recognition tasks, it lacks a probabilistic interpretation. In this paper, we present a probabilistic framework for 2DCCA called probabilistic 2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the parameters. Experimental results on synthetic and real data demonstrate superior performance in loading factor estimation for P2DCCA compared to 2DCCA. For real data, three subsets of AR face database and also the UMIST face database confirm the robustness of the proposed algorithm in face recognition tasks with different illumination conditions, facial expressions, poses and occlusions.