Comparing the Generating Strategies of Hydropower of Cascade Reservoirs to Mitigate the Shortage of Water Supply

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Policy support for wastewater use in Hanoi

Domestic wastewater use has the potential to be an important part of urban water management in Hanoi if it is supported by Vietnamese policy. This appears to be the case, with the water and environmental legislation stipulating discharge and treatment requirements; outlining financial incentives; and encouraging research, development and private participation in treatment and use. However, policies in potential reuse sectors, principally agriculture and aquaculture, do not reflect these positive assertions. Furthermore, inadequate financial provision for treatment that would facilitate planned use and stimulate private sector interest is also an impediment. In Hanoi, the result is that wastewater use takes place informally through the utilization of polluted water for agriculture and aquaculture. However, small changes could lead to safe, cost effective planned reuse which would protect the environment and provide Hanoi with a much needed water supply in the future

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Impacts of fallow conditions, compost and silicate fertilizer on soil nematode community in salt–affected paddy rice fields in acid sulfate and alluvial soils in the Mekong Delta, Vietnam

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)Avoidance of intensive rice cultivation (IRC) and soil amendments are potential practices to enhance soil properties. There is only limited information on the effects of reduced IRC and its mixture with compost or silicate fertilizer (Si) on the soil nematode community in salt–affected soils. This study aimed to assess the shifts of soil nematode community by reducing a rice crop from triple rice system (RRR) to a double rice system and mixed with compost or Si in paddy fields in acid sulfate soil (ASS) and alluvial soil (AL) in the Mekong Delta, Vietnam. Field experiments were designed with four treatments in four replicates, including RRR and a proposed system of double–rice followed by a fallow (FRR) and with 3 Mg ha–1 crop−1 compost or 100 kg ha–1 crop−1 Si. Soils were collected at harvest after the 2 year experiment, reflecting the fifth and third consecutive rice crop in RRR and FRR system, respectively. Results showed that reduced IRC gave a significant reduction in abundance of plant–parasitic nematodes (PPN), dominated by Hirschmanniella and increased abundance bacterivorous nematodes when mixed to compost and silicate fertilizer in ASS. In addition, reduced IRC increased nematode biodiversity Hill’s indices and reduced herbivorous footprint in ASS. Proposed system having compost or Si had strongly increased in bacterivorous and omnivorous footprints. Particularly, reduced IRC mixture with Si increased abundance of Rhabdolaimus, Mesodorylaimus and Aquatides, metabolic footprints (structure footprint, bacterivorous, omnivorous and predator) and diversity Hill’s N1 index in ASS. Our results highlighted that reduced IRC was a beneficial practice for decreasing abundance of PPN in salt-affected soils and increasing abundance of FLN in ASS. IRC mixture with compost or Si had potential in structuring the nematode communities with increasing biodiversity, trophic structure, and metabolic footprintsPeer reviewedFinal Accepted Versio