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31 |
Assessing the eco-efficiency of a circular economy system in Chinas coal mining areas: Emergy and data envelopment analysis
ارزیابی کارآیی سازگار با محیط زیست یک سیستم اقتصاد دایره ای در مناطق استخراج زغال سنگ چین: تجزیه و تحلیل اضطراری پوششی داده ها-2019 Improving the eco-efficiency of the circular economy system in mining areas has been recognized as the
most effective way to reduce the greenhouse effect and achieve sustainable development. Based on the
emergy theory and on data envelopment analysis (DEA), this paper adopts the SBM-Undesirable model
to evaluate the eco-efficiency of the circular economy system in Chinas largest coal mining area, Shanxi
Province, during the period 2006e2015. Emergy flow indices are treated as input and output indices.
Eco-efficiency is factorized into economic efficiency and environmental efficiency. The potential for
improvement of the circular economy system is analyzed based on input redundancy and output deficiency.
The results for the period 2006e2015 indicate the following: (1) both the input and the output of
the circular system increase during this period; (2) the increased input relies on mostly imported
emergy, and the increase inwaste emergy is lower than the exported emergy; (3) eco-efficiency is invalid
except for 2011 and 2012 and exhibits a decreasing trend beginning in 2013; (4) environmental efficiency
is invalid over the entire period, and the eco-efficiency level is positively related to the economic efficiency
score; and (5) the circular economy system has a larger energy saving space, and the key to
achieving sustainability of the circular economy system is output growth. Keywords: Emergy analysis | DEA | Eco-efficiency | Circular economy | Coal mining area |
مقاله انگلیسی |
32 |
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 |
مقاله انگلیسی |
33 |
Assessment of eco-geo-environment quality using multivariate data: A case study in a coal mining area of Western China
ارزیابی کیفیت محیط زیست با استفاده از داده های چند متغیره: یک مطالعه موردی در یک منطقه استخراج زغال سنگ در غرب چین-2019 Coal resource extensive development and utilization seriously threatens the region fragile ecological environment
in Western China. Using the Yushenfu coal mining area as a study site, the ecological and geological
environment (eco-geo-environment) factors affected by coal mining were analyzed. Spatiotemporal data extracted
from remote sensing images was combined with survey data to establish an evaluation and prediction
model. Integrated eco-geo-environmental quality was established by calculating the combined weight of each
factor using the fuzzy delphi analytic hierarchy process method to develop a comprehensive multi-factor ecogeo-
environment quality evaluation map. Results shows that the eco-geo-environment quality was divided in
five grades of worse, bad, medium, good and better, the overall condition of the study region is moderate and it
is apparent that regions with less intensive mining activities (Yushen sub coal mining area) are in better condition
as compared to those regions where intensive mining is well established (Shenfu sub coal mining area).
Comparisons with the classified eco-geo-environment categories revealed that different eco-geo-environment
quality grades were affected different eco-geo-environment categories after a period of coal resources exploitation,
but eco-geo-environment quality generally has been declining since the development and utilization
of coal resources. The model provides a more scientific and accurate method to evaluate regional eco-geoenvironmental
quality, which is important for the coordinated development between coal mining and the fragile
eco-environment. Keywords: Ecologically vulnerable coal mining area | Multi-criteria decision analysis | Eco-geo-environment type | Eco-geo-environment quality assessment |
مقاله انگلیسی |
34 |
A robust cutting pattern recognition method for shearer based on Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy
یک روش تشخیص الگوی برش قوی برای برش بر اساس ماشین بردار حداقل پشتیبانی مربع مجهز به بهینه سازی ذرات تغییر یافته Chaos و استراتژی تصحیح آنلاین-2019 Accurate cutting pattern recognition method for shearer in coal mining process has drawn more and
more attention over the past decades due to its important role in guaranteeing the steady operation
of the equipment, which, however, remains challenging caused by the mismatch of cutting pattern
recognition especially for dynamic uncertainty of future sampled data. Therefore, a novel approach
for cutting pattern recognition with an optimal Online Correcting Strategy (OCS) combined with Least
Square Support Vector Machine (LSSVM) and Chaos Modified Particle Swarm Optimization (CMPSO)
algorithm, named OCS-CMPSO-LSSVM, is proposed, where LSSVM models the functional relationship
between input and output of the system, CMPSO optimizes the parameters of LSSVM, and OCS
modifies the model to reduce its mismatch as the system runs, respectively. The performance of
the proposed model is demonstrated with a simulation experiment and compared with the existing
methods reported in the literature in detail. The experimental results reveal that the proposed models
can achieve better cutting pattern recognition performance and higher robustness. Keywords: Cutting pattern recognition | Model mismatch | Online Correcting Strategy (OCS) | Least Square Support Vector Machine | (LSSVM) | Chaos Modified Particle Swarm | Optimization algorithm (CMPSO) |
مقاله انگلیسی |
35 |
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 |
مقاله انگلیسی |
36 |
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 |
مقاله انگلیسی |
37 |
A novel shearer cutting pattern recognition model with chaotic gravitational search optimization
مدل تشخیص الگو برش برش رمان با بهینه سازی جستجوی گرانشی آشفته-2019 The accurate recognition of the shearer cutting pattern is the focus in fully mechanized coal mining.
Hence, a new cutting pattern recognition model based on the combination of Relevance Vector
Machine (RVM) and Chaotic Gravitational Search Algorithm (CGSA) is proposed. Initially, the motor operation
data, including voltage, current and motor speed, are collected as the detection signal and the RVM
classifier based on Bayesian framework is chosen for pattern recognition. In order to optimize the parameters
in RVM, which has a great influence on the performance of RVM, the optimization algorithm
Gravitational Search Algorithm (GSA) is introduced. Finally, the basic GSA is modified into CGSA with
the chaotic mapping for increasing the search diversity of the algorithm. The experimental study demonstrates
the advantageous performance of the proposed model even without any feature extraction
operations. Keywords: Cutting pattern recognition | Relevance Vector Machine (RVM) | Gravitational Search Algorithm (GSA) | Chaotic mapping |
مقاله انگلیسی |
38 |
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 |
مقاله انگلیسی |
39 |
ارزیابی سینتیک گاززدایی دی اکسید کربن بر اساس اندازه گیری های غیر همدما و مدلسازی CFD از آنالیزگر گرماسنجی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 35 هدف از این مطالعه تجزیه و تحلیل کامل از سینتیک فرآیند گاززدایی در جو 〖CO〗_2 بود. ابتدا، پارامترهای سینتیکی بر اساس داده¬های تجربی از اندازه¬گیری گرماسنجی غیرایزوترم (TGA) در سه سرعت گرمایش (20K⁄min ،10 ،3) با استفاده از روش Senum & Yang (SY) تعیین شد. سپس با توجه به کاستی¬های آنالیز گرماسنجی، مدل مبتنی بر CFD سه بعدی برای پیش¬بینی گرمایش و تجزیه نمونه زغال سنگ درون آنالیزگر TG تهیه شد. مدل توافق و سازش خوبی با داده¬های اندازه¬گیری نشان داده است. علاوه بر این، مدلسازی CFD نشان می¬د¬هد که استفاده سرعت گرمایش کمتر در طول آزمایشات TGA ممکن است پارامترهای سینتیکی دقیق¬تری را در مقایسه با اندازه¬گیری¬ها در سرعت گرمایش بالاتر تخمین بزند. این امر به این دلیل است که سرعت گرمایش بیشتر باعث ایجاد گرادیان دمایی بزرگتر در داخل کوره و در نتیجه اختلاف دمای بالاتر بین نمونه ذغال سنگ و دیواره گرمایشی می¬شود ( 11K برای K⁄min 3، 16K برای K⁄min 10، 18K برای K⁄min 20). علاوه بر این، اختلافات در سرعت گرمایش در طول آزمایش تأثیر معنی¬داری در دقت روش SY (خطای حداکثر 20٪) دارد. بنابراین، هنگامی که آزمایش¬های غیرایزوترم انجام می¬شود، روش معکوس (بر اساس انتگرال¬گیری عددی معادله واکنش و الگوریتم لونبرگ-مارکارت) برای تعیین پارامترهای سینتیکی ترجیح داده می¬شود. بدین ترتیب مقادیر انرژی اکتیواسیون که براساس دمای اندازه¬گیری شده (TG) محاسبه می¬شود برای سرعت گرمایش مورد استفاده از 7/253 تا 2/231 کیلوژول بر مول در نظر گرفته شد، در حالی که مقادیر تنظیم شده بر اساس دمای مدل CFD کمی پایین تر یعنی از 8/247 به 6/223 کیلوژول بر مول بود. دومین پارامترهای سینتیکی فاکتور پیش نُمایی نیز با افزایش سرعت گرمایش کاهش می¬یابد و در جایی بالاتر از مقادیر اصلاح شده براساس دمای نمونه مدل محاسبه می¬شود. کمترین اختلاف در پارامترهای سینتیکی محاسبه شده طی اندازه¬گیری در سرعت گرمایش K⁄min 3 مشاهده شد.
واژههای کلیدی: TGA | سینتیک گازی سازی CO2 | مدل سازی CFD |
مقاله ترجمه شده |
40 |
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 |
مقاله انگلیسی |