Natural Gas Production

Current data on natural gas production, as well as a breakdown of production by country. Updated on a monthly basis.     

Color determinants of surface stratification

Although color plays a crucial role in the demarcation of surfaces in the visual field, its role in depth perception is not well understood. Certain special effects of color on depth perception that arise from optical factors such as chromatic aberration (chromostereopsis) have been studied, but less is known about the role of perceptual factors of color in determining depth relations. The present study explores the role that the different attributes of color such as hue, chroma, and lightness play in the stratification of surfaces in depth. In two experiments, subjects manipulated specific dimensions of colors (hue, chroma, lightness, and whiteness) while making judgments of coplanarity of either two or more abutting surfaces. The results demonstrate that for surfaces to appear coplanar, their lightness has to be proportional to the natural (intrinsic) lightness of the hues. No meaningful effects of chroma, whiteness, or blackness were found in depth stratification. The results suggest a primary role of the natural lightness of hues in depth perception.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map

Residential natural gas consumption depends on several factors. Available tools and methods to identify, categorize, and validate effective factors have some limitations, making consumption modeling more complex. Once a comprehensive model of effective consumption factors is developed for residential gas consumers, it can predict consumption. In addition, such a model could be used to verify the accuracy of measuring devices in order to reduce unaccounted for gas (UFG). The key factors affecting residential gas consumption were identified based on previous studies and their mutual effects were analyzed using a fuzzy cognitive mapping (FCM) method. The most significant factors and their effects on natural gas consumption in the residential sector were determined. In this study, for the first time, the expected consumption for each consumer was estimated using a consumption index. Generally, if the estimated consumption is significantly different from the amount recorded by the meter, it could suggest a potential source of UFG. The proposed method was applied to the data collected from the residential gas consumers of a small region in Iran (Dasht-e Arjan region, Fars province), and the results demonstrate the effectiveness of the proposed method.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

Loliolide,a New Therapeutic Option for Neurological Diseases? In Vitro Neuroprotective and Anti-Inflammatory Activities of a Monoterpenoid Lactone Isolated from Codium tomentosum

Parkinsons Disease (PD) is the second most common neurodegenerative disease worldwide, and is characterized by a progressive degeneration of dopaminergic neurons. Without an effective treatment, it is crucial to find new therapeutic options to fight the neurodegenerative process, which may arise from marine resources. Accordingly, the goal of the present work was to evaluate the ability of the monoterpenoid lactone Loliolide, isolated from the green seaweed Codium tomentosum, to prevent neurological cell death mediated by the neurotoxin 6-hydroxydopamine (6-OHDA) on SH-SY5Y cells and their anti-inflammatory effects in RAW 264.7 macrophages. Loliolide was obtained from the diethyl ether extract, purified through column chromatography and identified by NMR spectroscopy. The neuroprotective effects were evaluated by the MTT method. Cells’ exposure to 6-OHDA in the presence of Loliolide led to an increase of cells’ viability in 40%, and this effect was mediated by mitochondrial protection, reduction of oxidative stress condition and apoptosis, and inhibition of the NF-kB pathway. Additionally, Loliolide also suppressed nitric oxide production and inhibited the production of TNF-α and IL-6 pro-inflammatory cytokines. The results suggest that Loliolide can inspire the development of new neuroprotective therapeutic agents and thus, more detailed studies should be considered to validate its pharmacological potential.     

Natural Gas Production

Current data on natural gas production, as well as a breakdown of production by country. Updated on a monthly basis.     

枪管结构对固有频率的影响

为了研究枪管结构对枪管固有频率的影响,针对枪管尤其是狙击枪管设计中约束长度、枪管外形、膛口装置以及膛线等不同结构,建立了相应的三维模型。采用以六面体为主的网格对枪管进行了网格划分,采用有限元方法对枪管进行了模态分析。计算获得了不同结构枪管的固有频率; 通过比较,得到了不同枪管结构对固有频率的影响。分析结果表明:枪管尾部螺纹长度越长,枪口振动幅度越低; 相同质量枪管,锥形外形枪管刚度最好; 加装膛口装置对枪管刚度和振动特性有不利影响。     

Taxonomy of digital community currency platforms*

ABSTRACT

Community currencies are used to pay for products or services within specific groups defined by geographical boundaries or specific common interests. Financial crises, social emergence in developing countries, and increased access to digital devices have stimulated a growing number of communities worldwide to develop digital currency projects. These projects use technologies ranging from traditional plastic cards to mobile phones and blockchain technologies. Following the design science research approach, this paper analyzes digital community currencies (DCCs) by developing a taxonomy based on platform architecture, governance, transactionality and virtuality. By investigating 22 DCC platforms around the world, 4 groups were distinguished: local, proprietary, commons and cyber. The identification of these four different groups of digital community currencies allows us to better discuss the potentials and limitations of each one of them. The presented taxonomy can be useful to researchers and practitioners both to explain and to design DCC platforms. Discussing each of the emerging categories from the proposed taxonomy helps us to provide insights into DCCs, offering a new theoretical frame for investigating the particular case of digital payment platforms.