Agriculture Diversification in the Mekong Delta: Farmers' Motives and Contributions to Livelihoods

Although specialization is the global trend in agriculture, integrated farming systems have emerged in Vietnam. An important motive was the desire to improve the livelihoods and the diet of the nuclear families; this was evident in the analysis using the household life cycle of five phases. Off-farm diversification was especially important for a new household. At the onset of expansion, the new mothers replaced off-farm with homebound activities. During expansion the farmers increased virtual farm size by keeping more livestock; during accumulation, they invested in land or education, and during consolidation old couples adjusted farm activities to their labor capacity. Livestock, including fish, was essential for livelihood. The distribution of goats instead of cattle by credit or by "passing-on-the-gift " was far more effective for poverty alleviation. Technological innovations on the cultivation of rice and fruits, and the breeding of fish were essential for change. The improved food security and reduced cash income from rice after the 1986 reforms pushed farmers to take risks. The farm area and number of component farm activities providing cash determined the level of cash income from agriculture. Farms with at least four flows of biomass between components earned more, demonstrating that real integration improved profits. A minimum area of land in, or close to, the homestead, and know-how are required for an effective integration of components.Mekong Delta, agricultural diversification

A comparative experimental design and performance analysis of Snort-based Intrusion Detection System in practical computer networks

As one of the most reliable technologies, network intrusion detection system (NIDS) allows the monitoring of incoming and outgoing traffic to identify unauthorised usage and mishandling of attackers in computer network systems. To this extent, this paper investigates the experimental performance of Snort-based NIDS (S-NIDS) in a practical network with the latest technology in various network scenarios including high data speed and/or heavy traffic and/or large packet size. An effective testbed is designed based on Snort using different muti-core processors, e.g., i5 and i7, with different operating systems, e.g., Windows 7, Windows Server and Linux. Furthermore, considering an enterprise network consisting of multiple virtual local area networks (VLANs), a centralised parallel S-NIDS (CPS-NIDS) is proposed with the support of a centralised database server to deal with high data speed and heavy traffic. Experimental evaluation is carried out for each network configuration to evaluate the performance of the S-NIDS in different network scenarios as well as validating the effectiveness of the proposed CPS-NIDS. In particular, by analysing packet analysis efficiency, an improved performance of up to 10% is shown to be achieved with Linux over other operating systems, while up to 8% of improved performance can be achieved with i7 over i5 processors

A QoS-based flow assignment for traffic engineering in software-defined networks

In order to meet a tremendous amount of data storage requirement in next-generation wireless networks, an increasing number of cloud data centers has been deployed around the world. The underlying core networks are expected to provide the ability to store data in a dynamic and scalable computing environment. The traditional Internet Protocol (IP) has shown to be restricted due to its static architecture, which accordingly motivates the development of Software-Defined Networks (SDNs). In the SDNs, Traffic Engineering (TE) is simpler and programmable with a controller without the requirement of reconfiguration for all network devices. However, the existing TE algorithm of the SDNs rejects a number of requested flows caused by their undetermined routing paths where only flow bandwidth is considered in path determination. This paper proposes a Quality-of-Service (QoS) based Flow Assignment algorithm which enables the computation of end-to-end path for traffic flows guaranteeing the QoS requirements including bandwidth, end-to-end delay and packet loss probability. Through the Open Source Hybrid IP/SDNs platform, the proposed algorithm is validated and shown to significantly reduce flow rejection rate of up to 50% compared to the conventional approach, and therefore can be used to implement an effective DiffServ mechanism for flow allocation in the SDNs

TransNets: Learning to Transform for Recommendation

Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender Systems (RecSys 2017

History-sensitive accumulation rules for life-time prediction under variable loading

This is the post-print version of the article. The official published version can be obtained from the link below - Copyright @ 2011 SpringerA general form of temporal strength conditions under variable creep loading is employed to formulate several new phenomenological accumulation rules based on the constant-loading durability diagram. Unlike the well-known Robinson rule of linear accumulation of partial life-times, the new rules allow to describe the life-time sensibility to the load sequence, observed in experiments. Comparison of the new rules with experimental data shows that they fit the data much more accurately than the Robinson rule

Designing Robust Hierarchically Textured Oleophobic Fabrics

Commercially available woven fabrics (e.g., nylon- or PET-based fabrics) possess inherently re-entrant textures in the form of cylindrical yarns and fibers. We analyze the liquid repellency of woven and nanotextured oleophobic fabrics using a nested model with n levels of hierarchy that is constructed from modular units of cylindrical and spherical building blocks. At each level of hierarchy, the density of the topographical features is captured using a dimensionless textural parameter D[subscript n][superscript *]. For a plain-woven mesh comprised of chemically treated fiber bundles (n = 2), the tight packing of individual fibers in each bundle (D[subscript 2][superscript *] ≈ 1) imposes a geometric constraint on the maximum oleophobicity that can be achieved solely by modifying the surface energy of the coating. For liquid droplets contacting such tightly bundled fabrics with modified surface energies, we show that this model predicts a lower bound on the equilibrium contact angle of θE ≈ 57° below which the Cassie–Baxter to Wenzel wetting transition occurs spontaneously, and this is validated experimentally. We demonstrate how the introduction of an additional higher order micro-/nanotexture onto the fibers (n = 3) is necessary to overcome this limit and create more robustly nonwetting fabrics. Finally, we show a simple experimental realization of the enhanced oleophobicity of fabrics by depositing spherical microbeads of poly(methyl methacrylate)/fluorodecyl polyhedral oligomeric silsesquioxane (fluorodecyl POSS) onto the fibers of a commercial woven nylon fabric.United States. Army Research Office (W911NF-13-D-0001