Perturbative Effective Theory in an Oscillator Basis?

The effective interaction/operator problem in nuclear physics is believed to be highly nonperturbative, requiring extended high-momentum spaces for accurate solution. We trace this to difficulties that arise at both short and long distances when the included space is defined in terms of a basis of harmonic oscillator Slater determinants. We show, in the simplest case of the deuteron, that both difficulties can be circumvented, yielding highly perturbative results in the potential even for modest (~6hw) included spaces.Comment: 10 pages, 4 figure

Effects of vermicompost on the growth and yield of spring onion (Allium fistulosum L.)

Spring onion (Allium fistulosum L.) is a popular salad vegetable produced widely over the world, including in Vietnam. Thanks to its flavor and aroma, it is an indispensable ingredient used to flavor soups and other dishes. Vermicompost is a natural and environmentally friendly fertilizer used widely to increase crop production and maintain the sustainability of agrosystems. Consequently, this study was conducted to investigate the efficiency of vermicompost at different application rates in promoting the growth and yield parameters of spring onion. The results show that adding vermicompost to spring onion production had significant positive effects on plant height, number of leaves, number of tillers, individual plant weight, and plot yield. Particularly, the application of vermicompost at 40 t ha-1 showed the highest performance in the observed parameters, increasing the number of leaves, number of tillers, individual plant weight, and plot yields to 64.78, 21.18, 302.96 g plant-1, and 4.86 kg m-2, respectively. The plot yields in the treatments of the highest and lowest vermicompost application increased by 49.1% and 3.9%, respectively, in comparison to the control. Consequently, there was a strongly positive relationship between the application rate of vermicompost and the plot yield

Effect of spent coffee grounds and liquid worm fertilizer on the growth and yield of Brassica campestris L.

Brassica campestris L. plants are widely grown, including in Asian countries where the leaves are used to prepare Chinese sour pickled mustard greens. The potential benefits of the application of organic by-products and organic fertilizers in sustainable agricultural production have been shown in previous studies. Consequently, this study investigated the effectiveness of liquid worm fertilizer (LWF) and spent coffee grounds (SCG) individually and in combination on the growth of B. campestris. The results showed that LWF at the highest dose had positive effects on the growth and yield of B. campestris, but SGC had inhibitory effects. The treatment consisting of composted SCG + triple of the standard dose of LWF resulted in the best plot yield with 3,866.7 g.plot-1, followed by the treatment of fresh SCG + triple of the standard dose of LWF, which produced a yield of 3,766.7 g.plot-1. The lowest yield (2,100.0 g.plot-1) was observed in the treatment of 1 kg.m-2 fresh SCG + no LWF. The interaction effect between SCG and LWF on the plot yield of B. campestris L. was significant (F(4,18) = 4.6; p = 0.01) demonstrating enhanced yield when both SCG and LWF were used in combination

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Semi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing Data

When floods hit populated areas, quick detection of flooded areas is crucial for initial response by local government, residents, and volunteers. Space-borne polarimetric synthetic aperture radar (PolSAR) is an authoritative data sources for flood mapping since it can be acquired immediately after a disaster even at night time or cloudy weather. Conventionally, a lot of domain-specific heuristic knowledge has been applied for PolSAR flood mapping, but their performance still suffers from confusing pixels caused by irregular reflections of radar waves. Optical images are another data source that can be used to detect flooded areas due to their high spectral correlation with the open water surface. However, they are often affected by day, night, or severe weather conditions (i.e., cloud). This paper presents a convolution neural network (CNN) based multimodal approach utilizing the advantages of both PolSAR and optical images for flood mapping. First, reference training data is retrieved from optical images by manual annotation. Since clouds may appear in the optical image, only areas with a clear view of flooded or non-flooded are annotated. Then, a semisupervised polarimetric-features-aided CNN is utilized for flood mapping using PolSAR data. The proposed model not only can handle the issue of learning with incomplete ground truth but also can leverage a large portion of unlabelled pixels for learning. Moreover, our model takes the advantages of expert knowledge on scattering interpretation to incorporate polarimetric-features as the input. Experiments results are given for the flood event that occurred in Sendai, Japan, on 12th March 2011. The experiments show that our framework can map flooded area with high accuracy (F1 = 96:12) and outperform conventional flood mapping methods

Effect of three types of growing media and vermicompost tea on the growth and individual weight of Chinese Kale (Brassica oleracea var. alboglabra Bailey)

This study aimed to examine the effects of growing media and vermicompost tea on the growth and yield of Chinese kale under pot conditions. Consequently, the experiment of the present study included six treatments, including a control treatment. The results showed that mixing cow manure and vermicompost with growing media and vermicompost tea application had significant positive effects on plant height, number of leaves, stem diameter, and individual weight of Chinese kale plants. The highest plant height, the number of leaves, and individual weight were seen in the T6 treatment [growing media 3 (the mixture of 50% soil, 20% cocopeat, and 30% vermicompost) + vermicompost tea application], but T2 treatment [growing media 2 (the mixture of 50% soil, 20% cocopeat, and 30% cow manure) + no vermicompost tea application] had the highest stem diameter. Noticeably, vermicompost tea application increased the individual weight of the plants in the T4 treatment (growing media of 100% soil + vermicompost tea application) by 119.9%, compared to the control (growing media of 100% soil + no vermicompost tea application). The findings demonstrated that vermicompost tea may have great potential for use as a foliar fertilizer for leaf vegetables to promote growth in plant height and weight

Uptake of groundwater nitrogen by a near-shore coral reef community on Bermuda

Nutrient enrichment can slow growth, enhance bioerosion rates, and intensify algal competition for reef-building corals. In areas of high human population density and/or limited waste management, submarine groundwater discharge can transfer anthropogenic nutrients from polluted groundwater to coastal reefs. In this case study, we investigate the impact of submarine groundwater discharge on a near-shore reef in Bermuda, where over 60% of sewage generated by the island’s 64,000 residents enters the groundwater through untreated cesspits. Temperature, salinity, pH, and alkalinity were monitored at a groundwater discharge vent, three locations across the adjacent coral reef (0–30 m from shore), and a comparison patch reef site 2 km from shore. Groundwater discharge was characterized by low salinity, low aragonite saturation state (Ω_(ar)), high alkalinity, elevated nitrate + nitrite (NO₃₋ + NO₂₋; hereafter, “NO₃₋”) concentrations (> 400 µM), and an elevated ¹⁵N/¹⁴N ratio of NO₃₋ (δ¹⁵N = 10.9 ± 0.02‰ vs. air, mean ± SD). Rainfall and tidal cycles strongly impacted groundwater discharge, with maximum discharge during low tide. NO₃₋ concentrations on the near-shore reef averaged 4 µM, ten times higher than that found at the control site 2 km away, and elevated NO₃₋ δ¹⁵N at the near-shore reef indicated sewage-contaminated groundwater as a significant nitrogen source. Tissue δ¹⁵N of Porites astreoides, a dominant reef-building coral, was elevated by ~ 3‰ on the near-shore reef compared to the control site, indicating that corals across the near-shore reef were assimilating groundwater-derived nitrogen. In addition, coral skeletal density and calcification rates across the near-shore reef were inversely correlated with NO₃₋ concentration and δ¹⁵N, indicating a negative coral health response to groundwater-borne nutrient inputs. P. astreoides bioerosion rates, in contrast, did not show an effect from the groundwater input