Distributed two-time-scale methods over clustered networks

In this paper, we consider consensus problems over a network of nodes, where the network is divided into a number of clusters. We are interested in the case where the communication topology within each cluster is dense as compared to the sparse communication across the clusters. Moreover, each cluster has one leader which can communicate with other leaders in different clusters. The goal of the nodes is to agree at some common value under the presence of communication delays across the clusters. Our main contribution is to propose a novel distributed two-time-scale consensus algorithm, which pertains to the separation in network topology of clustered networks. In particular, one scale is to model the dynamic of the agents in each cluster, which is much faster (due to the dense communication) than the scale describing the slowly aggregated evolution between the clusters (due to the sparse communication). We prove the convergence of the proposed method in the presence of uniform, but possibly arbitrarily large, communication delays between the leaders. In addition, we provided an explicit formula for the convergence rate of such algorithm, which characterizes the impact of delays and the network topology. Our results shows that after a transient time characterized by the topology of each cluster, the convergence of the two-time-scale consensus method only depends on the connectivity of the leaders. Finally, we validate our theoretical results by a number of numerical simulations on different clustered networks

Using 15N Isotope Technique for Study the Balance of Nitrogen Fertilizer in Young Rubber Tree -Hevea Brasilliencis

The pot experiment was conducted in late rainy season in the South of Vietnam to determine influence of 2gN/pot to %Na.e, Ndff and movement of N fertilizer in soil by using 15N isotope technique. There were big differences of 15Na.e, Ndff and Fertilizer N utilization in leaf, stem and root. 15Na.e of stem and leaf at 0.19% were higher than the one in root at 0.16%. Similarly, amount of Ndff also was highest in leaf at 0.331 g/pot. Total amount of N derived from fertilizer was 0.432g/pot and it means fertilizer N utilization was 21.09% while loses occupied very high at 44.75%. At the end of the study 90 days, N fertilizer had residue in soil at 25% and approximately 9.16% of N fertilizer was unaccounted

Designing a novel heterostructure AgInS₂@MIL-101(Cr) photocatalyst from PET plastic waste for tetracycline degradation

Semiconductor-containing porous materials with a well-defined structure could be unique scaffolds for carrying out selective organic transformations driven by visible light. We herein introduce for the first time a heterostructure of silver indium sulfide (AgInS(2)) ternary chalcogenide and a highly porous MIL-101(Cr) metal–organic framework (MOF) synthesised from polyethylene terephthalate plastic waste. Our results demonstrate that AgInS(2) nanoparticles were uniformly attached to each lattice plane of the octahedral MIL-101(Cr) structure, resulting in a nanocomposite with a high distribution of semiconductors in a porous media. We also demonstrate that the nanocomposite with up to 40% of AgInS(2) doping exhibited excellent catalytic activity for tetracycline degradation under visible light irradiation (∼99% tetracycline degraded after 4 h) and predominantly maintained its performance after five cycles. These results could promote a new material circularity pathway to develop new semiconductors that can be used to protect water from further pollution

How cheap can hygienic latrines be?

A construction and operation costing of 12 types of hygienic latrines widely used in rural areas of Vietnam and presented in the Hygienic latrine Manual of the Ministry of Health, using traditional construction materials has been conducted. The cost of latrines using traditional construction materials is ranging from USD37.5 (VIP) to USD194.4 (Septic tank constructed by brick for treatment of black and grey wastewater from sitting bowl toilet). Annually averted O&M costs of Vietnamese latrines range from USD1.86 (VIP) to USD 4.58 (wet latrine with septic tank) per capita per year. Costs of hygienic latrines can be further reduced, applying solutions such as using local materials for construction, reducing the tank volume by using the water-saving flushing devices or applying more frequent tank emptying services and mass production of latrine components. The less a hygienic latrine costs, the more chance for poor people in different places can get access to improved sanitation

One-pot preparation of alumina-modified polysulfone-graphene oxide nanocomposite membrane for separation of emulsion-oil from wastewater

In recent years, polysulfone-based nanocomposite membranes have been widely used for contaminated water treatment because they comprise properties such as high thermal stability and chemical resistance. In this study, a polysulfone (PSf) nanocomposite membrane was fabricated using the wet-phase inversion method with the fusion of graphene oxide (GO) and alumina (Al2O3) nanoparticles. We also showed that GO-Al2O3 nanoparticles were synthesised successfully by using a one-pot hydrothermal method. The nanocomposite membranes were characterised by Fourier transform infrared (FT-IR), scanning electron microscopy (SEM), nitrogen adsorption-desorption isotherms, energy-dispersive X-ray spectroscopy (EDX), thermogravimetric analysis (TGA), and water contact angle. The loading of GO and Al2O3 was investigated to improve the hydrophilic and oil rejection of the matrix membrane. It was shown that by using 1.5 wt.% GO-Al2O3 loaded in polysulfone, ~74% volume of oil was separated from the oil/water emulsion at 0.87 bar and 30 min. This figure was higher than that of the process using the unmodified membrane (PSf/GO) at the same conditions, in which only ~60% volume of oil was separated. The pH, oil/water emulsion concentration, separation time, and irreversible fouling coefficient (FRw) were also investigated. The obtained results suggested that the GO-Al2O3 nanoparticles loaded in the polysulfone membrane might have potential use in oily wastewater treatment applications

Structural <i>k</i>-means (S <i>k</i>-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data

Dramatic increases in climate data underlie a gradual paradigm shift in knowledge acquisition methods from physically based models to data-based mining approaches. One of the most popular data clustering/mining techniques is k-means, and it has been used to detect hidden patterns in climate systems; k-means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with “structured” data, that is, data in time and space domains, which are dominant in climate science. Here, we propose (i) a novel structural-similarity-recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrate that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we, for the first time, evaluated the “meaningfulness” of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with “structural” input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science.</p

HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts

By routing input tokens to only a few split experts, Sparse Mixture-of-Experts has enabled efficient training of large language models. Recent findings suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations. However, this strategy has two key limitations: (i) the policy derived from random routers might be sub-optimal, and (ii) it requires extensive resources during training and evaluation, leading to limited efficiency gains. This work introduces \HyperRout, which dynamically generates the router's parameters through a fixed hypernetwork and trainable embeddings to achieve a balance between training the routers and freezing them to learn an improved routing policy. Extensive experiments across a wide range of tasks demonstrate the superior performance and efficiency gains of \HyperRouter compared to existing routing methods. Our implementation is publicly available at {\url{{https://github.com/giangdip2410/HyperRouter}}}