Biological Treatment of Milk and Soybean Wastewater with Bioproducts

Dairy industries discharge larger amounts of wastewater as compared to other food industries. Wastewater contains high amount of total organic carbon materials and nutrients, such as fat, protein, and lactose. Biological treatment is widely used to treat this kind of wastewater due to the fluctuation of amount and content of dairy wastewater. This study investigated removal of total organic carbon (TOC) from two types of dairy wastewater-milk and soy milk wastewater. The bioproducts used in experiments were baker\u27s yeast, beer\u27s yeast, live liquid microorganism (LLMO), Enforcer Overnite Toilet Care Liquid, and Enforcer Overnite Toilet Care Granular. The parameters included in this study were shaking time, concentration of wastewater, types of wastewater and bioproducts. Overnite Toilet Care Granules and Baker\u27s yeast were very effective to remove TOC from milk wastewater. But when Overnite Toilet Care Granules dissolved, more particles were produced and increased the amount of TOC. So Baker\u27s yeast was more suitable to treat milk wastewater than the others. The best result is 57 of TOC removal and happed at 6 hours when concentration of TOC was 25 mg/l. G1 is the best bioproduct for TOC removal from soybean milk wastewater. 75.2 of TOC was removed by using G1. It was more than twice higher than TOC removal by using Baker\u27s yeast and Overnite Liquid Drain Care. Although the removal rate of using beer\u27s yeast is almost the same as using Baker\u27s yeast, Beer\u27s yeast did not show steady results. Beer\u27s yeast and Liquid Drain Care did not yield good results for treating both milk and wastewater. Because Beer\u27s yeast and Liquid Drain Care contained unknown components and low concentrations of bacteri

Biological Treatment of Milk and Soybean Wastewater with Bioproducts

Dairy industries discharge larger amounts of wastewater as compared to other food industries. Wastewater contains high amount of total organic carbon materials and nutrients, such as fat, protein, and lactose. Biological treatment is widely used to treat this kind of wastewater due to the fluctuation of amount and content of dairy wastewater. This study investigated removal of total organic carbon (TOC) from two types of dairy wastewater-milk and soy milk wastewater. The bioproducts used in experiments were baker\u27s yeast, beer\u27s yeast, live liquid microorganism (LLMO), Enforcer Overnite Toilet Care Liquid, and Enforcer Overnite Toilet Care Granular. The parameters included in this study were shaking time, concentration of wastewater, types of wastewater and bioproducts. Overnite Toilet Care Granules and Baker\u27s yeast were very effective to remove TOC from milk wastewater. But when Overnite Toilet Care Granules dissolved, more particles were produced and increased the amount of TOC. So Baker\u27s yeast was more suitable to treat milk wastewater than the others. The best result is 57 of TOC removal and happed at 6 hours when concentration of TOC was 25 mg/l. G1 is the best bioproduct for TOC removal from soybean milk wastewater. 75.2 of TOC was removed by using G1. It was more than twice higher than TOC removal by using Baker\u27s yeast and Overnite Liquid Drain Care. Although the removal rate of using beer\u27s yeast is almost the same as using Baker\u27s yeast, Beer\u27s yeast did not show steady results. Beer\u27s yeast and Liquid Drain Care did not yield good results for treating both milk and wastewater. Because Beer\u27s yeast and Liquid Drain Care contained unknown components and low concentrations of bacteri

Biological Treatment of Milk and Soybean Wastewater with Bioproducts

Dairy industries discharge larger amounts of wastewater as compared to other food industries. Wastewater contains high amount of total organic carbon materials and nutrients, such as fat, protein, and lactose. Biological treatment is widely used to treat this kind of wastewater due to the fluctuation of amount and content of dairy wastewater. This study investigated removal of total organic carbon (TOC) from two types of dairy wastewater-milk and soy milk wastewater. The bioproducts used in experiments were baker\u27s yeast, beer\u27s yeast, live liquid microorganism (LLMO), Enforcer Overnite Toilet Care Liquid, and Enforcer Overnite Toilet Care Granular. The parameters included in this study were shaking time, concentration of wastewater, types of wastewater and bioproducts. Overnite Toilet Care Granules and Baker\u27s yeast were very effective to remove TOC from milk wastewater. But when Overnite Toilet Care Granules dissolved, more particles were produced and increased the amount of TOC. So Baker\u27s yeast was more suitable to treat milk wastewater than the others. The best result is 57 of TOC removal and happed at 6 hours when concentration of TOC was 25 mg/l. G1 is the best bioproduct for TOC removal from soybean milk wastewater. 75.2 of TOC was removed by using G1. It was more than twice higher than TOC removal by using Baker\u27s yeast and Overnite Liquid Drain Care. Although the removal rate of using beer\u27s yeast is almost the same as using Baker\u27s yeast, Beer\u27s yeast did not show steady results. Beer\u27s yeast and Liquid Drain Care did not yield good results for treating both milk and wastewater. Because Beer\u27s yeast and Liquid Drain Care contained unknown components and low concentrations of bacteri

Control plane optimization in Software Defined Networking and task allocation for Fog Computing

As the next generation of mobile wireless standard, the fifth generation (5G) of cellular/wireless network has drawn worldwide attention during the past few years. Due to its promise of higher performance over the legacy 4G network, an increasing number of IT companies and institutes have started to form partnerships and create 5G products. Emerging techniques such as Software Defined Networking and Mobile Edge Computing are also envisioned as key enabling technologies to augment 5G competence. However, as popular and promising as it is, 5G technology still faces several intrinsic challenges such as (i) the strict requirements in terms of end-to-end delays, (ii) the required reliability in the control plane and (iii) the minimization of the energy consumption. To cope with these daunting issues, we provide the following main contributions. As first contribution, we address the problem of the optimal placement of SDN controllers. Specifically, we give a detailed analysis of the impact that controller placement imposes on the reactivity of SDN control plane, due to the consistency protocols adopted to manage the data structures that are shared across different controllers. We compute the Pareto frontier, showing all the possible tradeoffs achievable between the inter-controller delays and the switch-to-controller latencies. We define two data-ownership models and formulate the controller placement problem with the goal of minimizing the reaction time of control plane, as perceived by a switch. We propose two evolutionary algorithms, namely Evo-Place and Best-Reactivity, to compute the Pareto frontier and the controller placement minimizing the reaction time, respectively. Experimental results show that Evo-Place outperforms its random counterpart, and Best-Reactivity can achieve a relative error of <= 30% with respect to the optimal algorithm by only sampling less than 10% of the whole solution space. As second contribution, we propose a stateful SDN approach to improve the scalability of traffic classification in SDN networks. In particular, we leverage the OpenState extension to OpenFlow to deploy state machines inside the switch and minimize the number of packets redirected to the traffic classifier. We experimentally compare two approaches, namely Simple Count-Down (SCD) and Compact Count-Down (CCD), to scale the traffic classifier and minimize the flow table occupancy. As third contribution, we propose an approach to improve the reliability of SDN controllers. We implement BeCheck, which is a software framework to detect ``misbehaving'' controllers. BeCheck resides transparently between the control plane and data plane, and monitors the exchanged OpenFlow traffic messages. We implement three policies to detect misbehaving controllers and forward the intercepted messages. BeCheck along with the different policies are validated in a real test-bed. As fourth contribution, we investigate a mobile gaming scenario in the context of fog computing, denoted as Integrated Mobile Gaming (IMG) scenario. We partition mobile games into individual tasks and cognitively offload them either to the cloud or the neighbor mobile devices, so as to achieve minimal energy consumption. We formulate the IMG model as an ILP problem and propose a heuristic named Task Allocation with Minimal Energy cost (TAME). Experimental results show that TAME approaches the optimal solutions while outperforming two other state-of-the-art task offloading algorithms

The Role of Inter-Controller Traffic for Placement of Distributed SDN Controllers

We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Besides the Openflow control traffic exchanged between controllers and switches, we focus on the control traffic exchanged among the controllers in the cluster, needed to run coordination and consensus algorithms to keep the controllers synchronized. We estimate the effect of the inter-controller communications on the reaction time perceived by the switches depending on the data-ownership model adopted in the cluster. The model is accurately validated in an operational Software Defined WAN (SDWAN). We advocate a careful placement of the controllers, that should take into account both the above kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controllers placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Our work provides novel quantitative tools to optimize the planning and the design of the network supporting the control plane of SDN networks, especially when the network is very large and in-band control plane is adopted. We also show that for operational distributed controllers (e.g. OpenDaylight and ONOS), the location of the controller which acts as a leader in the consensus algorithm has a strong impact on the reactivity perceived by switches.Comment: 14 page

Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation

Few-shot segmentation (FSS) aims to segment objects of new categories given only a handful of annotated samples. Previous works focus their efforts on exploring the support information while paying less attention to the mining of the critical query branch. In this paper, we rethink the importance of support information and propose a new query-centric FSS model Adversarial Mining Transformer (AMFormer), which achieves accurate query image segmentation with only rough support guidance or even weak support labels. The proposed AMFormer enjoys several merits. First, we design an object mining transformer (G) that can achieve the expansion of incomplete region activated by support clue, and a detail mining transformer (D) to discriminate the detailed local difference between the expanded mask and the ground truth. Second, we propose to train G and D via an adversarial process, where G is optimized to generate more accurate masks approaching ground truth to fool D. We conduct extensive experiments on commonly used Pascal-5i and COCO-20i benchmarks and achieve state-of-the-art results across all settings. In addition, the decent performance with weak support labels in our query-centric paradigm may inspire the development of more general FSS models. Code will be available at https://github.com/Wyxdm/AMNet.Comment: Accepted to NeurIPS 202