Qualitative model of a positive hydrogen peroxide ion in a thermal bath

A qualitative model is proposed for a pair of atoms: oxygen and hydrogen in a single-mode optical cavity, bound by one valence electron and immersed in a thermal bath. The interaction of an electron with the cavity field depends on the state of the nuclei, which, in turn, is determined by the temperature of the phonon mode of the thermal bath. Computer simulation of the quantum dynamics of such a system shows the stable nature of the formation of both a stable molecular ion and a separate neutral oxygen atom and a positive hydrogen ion.Comment: 11 pages, 5 figure

Co-gasification of woody biomass and chicken manure: Syngas production, biochar reutilization, and cost-benefit analysis

The management and disposal of livestock manure has become one of the top environmental issues at a global scale in line with the tremendous growth of poultry industry over the past decades. In this work, a potential alternative method for the disposal of chicken manure from Singapore local hen layer farms was studied. Gasification was proposed as the green technology to convert chicken manure into clean energy. Through gasification experiments in a 10 kW fixed bed downdraft gasifier, it was found that chicken manure was indeed a compatible feedstock for gasification in the presence of wood waste. The co-gasification of 30 wt% chicken manure and 70 wt% wood waste produced syngas of comparable quality to that of gasification of pure wood waste, with a syngas lower heating value (LHV) of 5.23 MJ/Nm3 and 4.68 MJ/Nm3, respectively. Furthermore, the capability of the gasification derived biochar in the removal of an emerging contaminant (artificial sweetener such as Acesulfame, Saccharin and Cyclamate) via adsorption was also conducted in the second part of this study. The results showed that the biochar was effective in the removal of the contaminant and the mechanism of adsorption of artificial sweetener by biochar was postulated to be likely via electrostatic interaction as well as specific interaction. Finally, we conducted a cost-benefit analysis for the deployment of a gasification system in a hen layer farm using a Monte Carlo simulation model

Collapse of dark states in Tavis-Cummings model

The singlet state of a system of two two-level atoms changes smoothly, remaining dark, as the Hamiltonian TC is slowly deformed, despite the inapplicability of the adiabatic theorem to this case. In this case, there is a small probability of emission of free photons, which does not depend on the smoothness of the deformation of the Hamiltonian. The effect of spontaneous emission is enhanced by the addition of one more pair of atoms in the singlet state due to the exchange of virtual photons in the cavity. A similar effect was also established for the case when atoms can move between two cavities, but here, on the contrary, with an increase in the number of atoms, the emission decreases. This purely quantum effect must be taken into account in practical manipulations with atomic singlets; however, its weakness testifies, rather, to the stability of dark states and the prospects for their use in information exchange (quantum cryptographic protocols) and as an energy accumulator for nono-devices.Comment: 16 pages, 14 figure

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

Urbanization often occurs in an unplanned and uneven manner, resulting in profound changes in patterns of land cover and land use. Understanding these changes is fundamental for devising environmentally responsible approaches to economic development in the rapidly urbanizing countries of the emerging world. One indicator of urbanization is built-up land cover that can be detected and quantified at scale using satellite imagery and cloud-based computational platforms. This process requires reliable and comprehensive ground-truth data for supervised classification and for validation of classification products. We present a new dataset for India, consisting of 21,030 polygons from across the country that were manually classified as “built-up” or “not built-up,” which we use for supervised image classification and detection of urban areas. As a large and geographically diverse country that has been undergoing an urban transition, India represents an ideal context to develop and test approaches for the detection of features related to urbanization. We perform the analysis in Google Earth Engine (GEE) using three types of classifiers, based on imagery from Landsat 7 and Landsat 8 as inputs. The methodology produces high-quality maps of built-up areas across space and time. Although the dataset can facilitate supervised image classification in any platform, we highlight its potential use in GEE for temporal large-scale analysis of the urbanization process. Our methodology can easily be applied to other countries and regions

A Review of Characteristics of Bio-Oils and Their Utilization as Additives of Asphalts

Transforming waste biomass materials into bio-oils in order to partially substitute petroleum asphalt can reduce environmental pollution and fossil energy consumption and has economic benefits. The characteristics of bio-oils and their utilization as additives of asphalts are the focus of this review. First, physicochemical properties of various bio-oils are characterized. Then, conventional, rheological, and chemical properties of bio-oil modified asphalt binders are synthetically reviewed, as well as road performance of bio-oil modified asphalt mixtures. Finally, performance optimization is discussed for bio-asphalt binders and mixtures. This review indicates that bio-oils are highly complex materials that contain various compounds. Moreover, bio-oils are source-depending materials for which its properties vary with different sources. Most bio-oils have a favorable stimulus upon the low temperature performance of asphalt binders and mixtures but exhibit a negative impact on their high-temperature performance. Moreover, a large amount of oxygen element, oxygen-comprising functional groups, and light components in plant-based bio-oils result in higher sensitivity to ageing of bio-oil modified asphalts. In order to increase the performance of bio-asphalts, most research has been limited to adding additive agents to bio-asphalts; therefore, more reasonable optimization methods need to be proposed. Furthermore, upcoming exploration is also needed to identify reasonable evaluation indicators of bio-oils, modification mechanisms of bio-asphalts, and long-term performance tracking in field applications of bio-asphalts during pavement service life

Dynamic response analysis of rutting resistance performance of high modulus asphalt concrete pavement

In order to systematically study the rutting resistance performance of High-Modulus Asphalt Concrete (HMAC) pavements, a finite element method model of HMAC pavement was established using ABAQUS software. Based on the viscoelasticity theory of asphalt, the stress and deformation distribution characteristics of HMAC pavement were studied and compared to conventional asphalt pavement under moving loads. Then, the pavement temperature field model was established to study the temperature variation and the thermal stress in HMAC pavement. Finally, under the condition of continuous temperature variation, the creep behavior and permanent deformation of HMAC pavement were investigated. The results showed that under the action of moving loads, the strain and displacement generated in HMAC pavement were lower than those in conventional asphalt pavement. The upper surface layer was most obviously affected by outside air temperature, resulting in maximum thermal stress. Lastly, under the condition of continuous temperature change, HMAC pavement could greatly reduce the deformation of asphalt material in each surface layer compared to conventional asphalt pavement

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

When directly using existing text generation datasets for controllable generation, we are facing the problem of not having the domain knowledge and thus the aspects that could be controlled are limited. A typical example is when using CNN/Daily Mail dataset for controllable text summarization, there is no guided information on the emphasis of summary sentences. A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with a deep understanding of the domain knowledge. Motivated by this vision, our paper introduces a new text generation dataset, named MReD. Our new dataset consists of 7,089 meta-reviews and all its 45k meta-review sentences are manually annotated with one of the 9 carefully defined categories, including abstract, strength, decision, etc. We present experimental results on start-of-the-art summarization models, and propose methods for structure-controlled generation with both extractive and abstractive models using our annotated data. By exploring various settings and analyzing the model behavior with respect to the control signal, we demonstrate the challenges of our proposed task and the values of our dataset MReD. Meanwhile, MReD also allows us to have a better understanding of the meta-review domain.Comment: 15 pages, 5 figures, accepted at ACL 202