Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach
ویژگی های جذب CO2 / CH4 فوق بحرانی در انواع مختلف ذغال سنگ و رویکرد یادگیری ماشین-2019
The injection of CO2 into deep coal beds can not only improve the recovery of CH4, but also contribute to the geological sequestration of CO2. The adsorption characteristics of coal determine the amount of the greenhouse gas that deep coal seams can store in place. Using self-developed adsorption facility of supercritical fluids, this paper studied the adsorption behavior of supercritical CO2 and CH4 on three types of coal (anthracite, bituminous coal A, bituminous coal B) under different temperatures of 35 °C, 45 °C and 55 °C. The influence of temperature, pressure, and coal rank on the Gibbs excess and absolute/real adsorption amount of supercritical CO2/CH4 on coal samples has been analyzed. Several traditional isotherm models are applied to interpret the experimental data and Langmuir related models are verified to provide good performances. However, these models are limited to isothermal conditions and are highly depended on extensive experiments. To overcome these deficiencies, one innovative adsorption model is proposed based on machine learning methods. This model is applied to the adsorption data of both this paper and four early publications. It was proved to be highly effective in predicting adsorption behavior of a certain type of coal. To further break the limit of coal type, the second optimization model is provided based on published data. Using the second model, one can predict the adsorption behavior of coal based on the fundamental physicochemical parameters of coal. Overall, working directly with the real data, the machine learning technique makes the unified adsorption model become possible, avoiding tedious theoretical assumptions, derivations and strong limitations of the traditional model.
Keywords: Supercritical CO2 | Supercritical CH4 | Coal | Adsorption model | Machine learning
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
ISeeU: یادگیری عمیق قابل تفسیر برای پیش بینی مرگ و میر در بخش مراقبت های ویژه-2019
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight into the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multiscale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Results show our model attains a ROC AUC of 0.8735 (± 0.0025) which is competitive with the state of the art of Deep Learning mortality models trained on MIMIC-III data, while remaining interpretable. Supporting code can be found at https://github.com/ williamcaicedo/ISeeU.
Keywords: Deep learning | MIMIC-III | ICU | Shapley Values
Machine learning for measurement-based bandwidth estimation
یادگیری ماشین برای تخمین پهنای باند مبتنی بر اندازه گیری-2019
The dispersion that arises when packets traverse a network carries information that can reveal relevant network characteristics. Using a fluid-flow model of a bottleneck link with first-in first-out multiplexing, accepted probing tools measure the packet dispersion to estimate the available bandwidth, i.e., the residual capacity that is left over by other traffic. Difficulties arise, however, if the dispersion is distorted compared to the model, e.g., by non-fluid traffic, multiple bottlenecks, clustering of packets due to interrupt coalescing, and inaccurate time-stamping in general. It is recognized that modeling these effects is cumbersome if not intractable. This motivates us to explore the use of machine learning in bandwidth estimation. We train a neural network using vectors of the packet dispersion that is characteristic of the available bandwidth. Our testing results reveal that even a shallow neural network identifies the available bandwidth with high precision. We also apply the neural network under a variety of notoriously difficult conditions that have not been included in the training, such as randomly generated networks with the multiple bottleneck links and heavy cross traffic burstiness. Compared to two state-of-the-art model-based techniques as well as a recent machine learning-based technique (Yin et al., 2016), our neural network approach shows improved performance. Further, our neural network can effectively control the estimation procedure in an iterative implementation. We also evaluate our method with other supervised machine learning techniques.
Keywords: Available bandwidth estimation | Machine learning
The rehabilitation of the mentally disabled in the community act in Israel: Entrepreneurship, leadership, and capitalizing on opportunities in policy making
بازسازی معلول ذهنی در عمل جامعه در اسرائیل: کارآفرینی، رهبری، و سرمایه گذاری در فرصت ها در سیاست گذاری-2019
This paper examines the role of policy entrepreneurs in the formation of a rehabilitation program in the field of mental health in Israel, shedding light on their role in general and specifically in mental health policy formation. Our research is based on a historical case study. The legislation process was examined through interviews with key actors in the legislative process and archival materials. While in general our findings reinforced existing literature, our research also revealed new information on several topics: organizations as policy entrepreneurs; inter-sectorial coalitions of entrepreneurs; and possible problems arising from the concept of ‘leadership by example.
Keywords: Policy formation | Policy entrepreneurs | Mental health policy | Mental health rehabilitation
Entrepreneurship under siege in regional communities: Evidence from Moranbah in Queensland, Australia
کارآفرینی تحت محاصره در جوامع منطقه ای: شواهدی از Moranbah در کوئینزلند ، استرالیا-2019
The notion that entrepreneurial activity is an important driving force for facilitating development is not new. To date, influential empirical research that is focused on the small community context of the entrepreneurship debate is still emerging. Moranbah in Queensland, Australia, is a case example of a small mining town that is characterised by a two-stream economy: the prosperous mining-employed stream and the much less prosperous non-mining employed stream. In as much as the local entrepreneurship stream in Moranbah offers a potential driving force to rebalance these two extremes in the current economy, the distinct prevalent conditions in the region pose threats that undermine the capacity of local entrepreneurs. This paper presents empirical evidence of how the socio-spatial, economic, political and cultural environments in Moranbah has put its entrepreneurship stream under a siege. The study found three key threats to entrepreneurship in Moranbah: (i) the nature of the ties with all levels of government, (ii) lack of status for local entrepreneurs, and (ii) the current business model direction taken by the coal mining industry. The results of the study have potential implications for current discussions around regional development policies.
Keywords: Entrepreneurship | Communities | Regional development | Moranbah | Government | Resource
Modeling of big production data storage of fully mechanized mining equipment based on workflow driven deep coupling network
مدل سازی ذخیره سازی داده های تولیدی بزرگ از کاوش تجهیزات مکانیکی بر مبنای شبکه ارتباطی عمیق جریان کار-2018
As the main equipment in coal production, the Fully Mechanized Mining Equipment is a typical multi-stage and multicomponents electromechanical system. Any component’s failure will lead to the unsafe production environment and low productive efficiency. With the flash development of information technology, the production data can be collected is growing exponentially. For the multi-stage and multi-components electromechanical system, how to effectively manage and use the production data which reflects the nature of the complex production system has become a difficult issue due to the massive and unorganized product data, and the complicated interactive effects among different stages and components. Aiming at this issue, a structural data storage and application framework is proposed based on complex network theory. According to the lifecycle of production data, the data management framework is divided into seven layers, including hardware layer, model layer, discrete data layer, relevancy data layer, application layer, decision support layer and feedback control layer. The sevenlayer framework reflects the process of data modeling, configuring, generating, relating and utilizing. The objective of the framework is to support the data management of an equipment manage and maintain decision-making software in the multi-components Fully Mechanized Mining Equipment system
Keywords: Fully Mechanized Mining Equipment; stuctural data storage; multi-components; complex network; equipment maintain
Adaptation opportunities and maladaptive outcomes in climate vulnerability hotspots of northern Ghana
فرصت های سازگاری و خروجی های ناسازگاری در نقاط حساس آسیب پذیری های آب و هوایی شمال غنا-2018
How climate change adaptation practices can constrain development and deliver maladaptive outcomes in vulnerability hotspots is yet to be explored in-depth using case study analyses. This paper explores the effects of climate change coping and adaptation responses in three case study villages across the Central Gonja district of northern Ghana. The study addresses the following research questions: i) What are the key climatic and non-climatic stressors confronting households in northern Ghanaian communities? ii) How are households adapting to climatic and non-climatic stressors? and iii) What are the outcomes of these coping and adaptation responses on development? The study employs a mixed-method approach including key informant interviews, focus group discussions and household questionnaire surveys. Data identified socioeconomic stressors including a lack of access to (and high cost of) farm inputs, labour shortages and population growth. Climatic stressors include erratic rainfall, high temperature, droughts and floods. Climatic and non-climatic stressors interact to affect agricultural practices and related livelihoods. The study identified various adaptation measures including extensification and intensification of agriculture, temporary migration, planting of drought resistant varieties, irrigation, and livelihood diversification. We show that many coping measures (e.g. livelihood diversifications activities such as selling of firewood and charcoal production) and adaptation responses (including intensification, extensification and irrigation) currently deliver maladaptive outcomes, resulting in lock-ins that could exacerbate future climate vulnerabilities. The paper contributes to the growing literature on adaptation and climate risk management by providing empirical evidence showing how coping and adaptations measures can deliver maladaptive outcomes in vulnerable communities.
keywords: Maladaptation |Climate change and variability |Livelihoods |Mixed methods |Africa
Simulating mining-induced strata permeability changes
شبیه سازی تغییرات نفوذپذیری اقشار ناشی از معادن-2018
Mining processes fracture the surrounding strata and may modify the flow of groundwater by inducing new fractures or changing the permeability of existing defects. The result of mining-induced permeability changes can be disturbance to aquifers or other surface or sub-surface water bodies. Traditional methods for predicting mining-induced fracture connectivity and enhanced permeability based on empirical strain-based criteria may not satisfy modern regulatory demands, nor adequately reflect local geological, geotechnical and hydro geological conditions. Standard continuum numerical methods may indirectly estimate permeability enhance ment from plastic strains however they are not able to track aperture on flow paths or predict fracture con nectivity. This paper presents a numerical approach that is demonstrated to be capable of representing longwall mining induced fracturing in sedimentary rock masses. By initiating and propagating fractures, determining connectivity and calculating aperture in a piecewise manner on flow paths, we have estimated permeability enhancement from first principles. Fracture intensity and porosity metrics are calculated and identify the height of the enhanced permeability fractured zone above a longwall goaf. Permeability within the overburden is estimated from the Kozeny-Carman permeability–porosity equation. At a mine site studied in detail in this paper a permeability increase from the in situ state is predicted to range from approximately eight orders-of-magnitude in the caved zone to one to two orders-of-magnitude in the strata above the fractured zone. Realistically si mulating cracking, fracturing and crushing of rock strata remains numerically intensive and challenging at the scale of a longwall panel. It is demonstrated in this paper and provides valuable insights into the rockmass response to mining.
Keywords: Coal mining permeability changes ، Coal mining ، Kozeny-Carman ، PFC ، Aquifer interference ، Fracture propagation
A meta-analysis of coal mining induced subsidence data and implications for their use in the carbon industry
یک متا تحلیل از معدن زغال سنگ ناشی داده فرونشست و مفاهیم برای استفاده از آنها در صنعت کربن-2018
Many empricial subsidence estimation tools exist worldwide but are designed and calibrated for specific coal fields. This paper presents an universal tool for the estimation of maximum subsidence (SMax). The subsidence tool is based on pooling and meta-analysis of empirical data from a number of different countries and coalfields. The key factors influencing SMax are the void dimensions and the mechanical competency of the overburden. These factors are used to estimate subsidence using the empirical equation SMax = [c/(1 + 10^(−a((W/ D) − b)))] ∗m, where W is the width of the void, D the depth, m the effective void thickness, and a, b, c are parameters related to the mechanical competency of the overburden. This universial empirical method was validated against historical data from United Kingdom and Australia. The method also provided SMax estimations for underground coal gasification (UCG) projects, that were inline with those from numerical modelling under certain conditions. This tool would likely be most useful when investigating areas, where there are little or no historical data of subsidence and mining. Such areas are most likely to be targeted by UCG schemes.
Keywords: Subidence ، UCG ، Estimation ، Meta-analysis ، Data
The economics of attitudes: A different approach to utility functions of players in tourism marketing coalitional networks
اقتصادهای برخوردها: یک دیدگاه متفاوت برای توابع استفاده بازیگران در شبکه های ائتلافی بازاریابی گردشگری-2018
The foundation of destination collaboration is based on the interdependency of the organizations involved in producing destination products. The high rate of destination collaboration failure underscores the need for conflict studies. Unlike previous studies, which depend solely on the collaboration monetary values, this study proposes a new approach to define its utility functions based on the attitudinal and motivational values. We employ the network theory to define the utility function of four major players and the game theory to examine three distribution solutions of coalitional activities values. The results support the notion of “free riders” mentioned in collaboration studies and explains why free riding is a natural phenomenon in tourism destinations’ marketing activities. The findings suggest that individual entities and hospitality are the two players with the highest admission fee and the least contribution. We suggest the concepts of fairness and stability to be considered in incentive policies to encourage collaboration among higher admission players.
keywords: Destination |Collaboration |Free riders |Conflict |Coalitional game |Network theory