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1 |
Hybrid governance and performances of environmental accounting
دولت هیبریدی و اجرای حسابداری محیط زیست-2021 Multiple centers of authority in hybrid forms create conditions of radical openness where questions of value and
fitness are in flux. Environmental accounting is suggested as a condition for steadying hybrid forms and opening
up possibilities for institutional innovations. This paper advances a critical social science analysis of environ-
mental accounting to help specify how, when, and in what ways strengthening accounting capacity advances
hybrid governance. Social studies of accounting argue that accounting systems are contingent on institutions:
rules and social conventions, not only data or science. Our practice-centered analysis of two cases of building
environmental accounting tools to advance high profile institutional innovations in US agri-environmental
governance finds that the systems of rules that structure and legitimize accounting protocols are not pre-
given. The same radical openness that presents opportunities for hybridity also reinforces uncertainties in
building accounting standards. We identify two major frictions: a) Conventions for determining technical
consensus and b) Rules for determining levels of transaction costs. We conclude by identifying a need to think
about hybrid forms critically. Although hybrid forms are an expression of creativity and collaboration, they are
also performances of a certain contemporary political covenant that delegitimizes state-centered governance. The
challenge ahead is to understand when and where hybrid arrangements add to socio-ecological regulation and
where they undermine the possibility of more functional approaches through a performance of seriousness. keywords: حکومتداری محیط زیست | کشاورزی | حسابداری | معیارهای | تغییرات اقلیمی | مسئوليت | Environmental governance | Agriculture | Accounting | Metrics | Climate change | Accountability |
مقاله انگلیسی |
2 |
Development and validation of INTENSS, a need-supportive training for nurses to support patients self-management
توسعه و اعتبار سنجی های آمیز، آموزش نیازمندی برای پرستاران برای حمایت از خود مدیریت بیماران-2021 Background: The growing prevalence of chronic illnesses requires nurses to support self-management and help
patients integrate the chronic illness into their life. To our knowledge there are currently no training programs
that combine the necessary components to adequately enhance nurses competencies in self-management
support.
Objective: The systematic development and validation of a need-supportive training in self-management support
for nurses.
Design: A three-phased study, according to van Meijel et al. (2004), with collection of building blocks, design, and
validation of the need-supportive character of the training.
Setting and participants: Eight training groups with 30 nurses, 34 nursing students and nine social healthcare
workers from different nursing colleges in Flanders, Belgium.
Methods: In phase one a literature review, current practice analysis, and problem and needs analysis were per-
formed. In phase two, the INTENSS training intervention was developed, framed within the Self-Determination
Theory and the 5A’s-Model. The training consisted of a basic training module and a video-interaction guidance
module. The intervention was subsequently tested in eight training groups (N = 73). Participants provided
feedback during focus group discussions. The intervention was cyclically adapted to trainees experiences and
suggestions. In phase three, we evaluated the need-supportive character of the training intervention.
Results: Phase one indicated the need for training, since nurses application of self-management support was
limited and practiced from a narrow medical point of view. In phase two we developed a theory-driven and
multifaceted training, building on attitude, knowledge, skills and reflection in the training. The training was
framed within the Self-Determination Theory both at the didactical level as well as on content and format.
Overall, participants appreciated the building blocks of the training as supporting their basic needs for auton-
omy, relatedness and competence.
Conclusions: INTENSS, a multifaceted need-supportive training in self-management support was developed,
successfully taking into account participants needs. keywords: توسعه مداخله | آموزش | تحصیلات | نظریه خود تعیین | پشتیبانی از خود مدیریت | مراقبت های بهداشتی | پرستاری | بیماری مزمن | Intervention development | Training | Education | Self-determination theory | Self-management support | Health care | Nursing | Chronic illness |
مقاله انگلیسی |
3 |
Online adaptive water management fault diagnosis of PEMFC based on orthogonal linear discriminant analysis and relevance vector machine
تشخیص خطای مدیریت آب انطباقی آنلاین PEMFC بر اساس تجزیه و تحلیل تمایز خطی متعامد و دستگاه بردار ارتباط-2020 A data-driven strategy for characterizing the water management failure in a Proton Exchange
Membrane Fuel Cell (PEMFC) is presented in this paper. To carry out the diagnosis
of water management failure, first the original single cell voltages are projected into lowerdimension
features by applying orthogonal linear discriminant analysis (OLDA). Then, a
classification methodology termed relevance vector machine (RVM) is employed to classify
the lower-dimension features into different categories that indicate the respective health
states of the system. The initially trained projecting vectors and classifiers lose their efficiency
gradually the characteristics of PEMFC system change, such as the cell voltages
decaying with time due to the normal degradation due to aging. An online adaptive diagnostic
strategy based on the posterior probability of RVM is proposed, so as to keep the
diagnostic accuracy over time. The efficiency and reliability of this online adaptive diagnostic
strategy is validated using an experimental database from a 90-cell PEMFC stack. Keywords: Proton exchange membrane fuel cell | (PEMFC) | Orthogonal linear discriminant | analysis (OLDA) | Relevance vector machine (RVM) | Water management failure | Online adaptive diagnostics |
مقاله انگلیسی |
4 |
Abundant cross reactivity in DNA circuits: An efficient and universal strategy to develop sensor arrays
واکنش متقاطع فراوان در مدارهای DNA: یک استراتژی کارآمد و جهانی برای توسعه آرایه های حسگر-2019 Artificial sensor arrays are useful in various applications of biomolecules recognition, but their abilities for
pattern recognition are often limited by the insufficient cross reactivity in sensors elements. Herein, the abundant
cross reactivity in DNA circuits has been successfully demonstrated and employed in sensor arrays to
discriminate various biomolecules including DNA analogues, RNA analogues, gene expression profiles and SNP
(Single Nucleotide Polymorphism) targets. In these DNA circuits, a hybridization chain reaction was employed
for target recognition, while an entropy-driven amplification process acted as a universal fluorescence output.
The fluorescence response patterns were further processed by linear discriminant analysis (LDA) or principal
component analysis (PCA), and all the targets were discriminated from each other without any overlap (95%
confidence). The sensor elements have been enriched by introduction of mismatches (Sensor Array 1), combination
of sensor units (Sensor Array 2), application of orthogonal reactivity (Sensor Array 3) and addition of
a new module (Sensor Array 4). Due to the great diversity and limitless opportunity, the powerful cross-reactive
DNA circuits have a number of advantages over the classical approaches to construct sensor arrays for biomolecules
pattern recognition. Keywords: Sensor arrays Cross reactivity | Hybridization chain reaction | Linear discriminant analysis | DNA circuits |
مقاله انگلیسی |
5 |
Classification of drilling stick slip severity using machine learning
طبقه بندی شدت لغزش چوب با استفاده از یادگیری ماشین-2019 Rate of penetration (ROP) is a key metric used to monitor the success of drilling a well. It is directly affected by
drilling vibrations since excessive vibrations result in a reduction of ROP. Vibration modeling and monitoring is
a complex process often requiring many simplifying assumptions that may not always generalize to different
BHAs, reservoirs, geology and formations. Therefore, it would be desirable to minimize drill string vibrations
using data driven models using readily available drilling data. The hypothesis tested is the classification of stick
slip severity due to drilling vibrations using open source machine learning algorithms. The stick slip index (SSI) –
measuring the severity of stick slip due to drilling vibrations – is classified as low or high using machine learning
classification algorithms such as logistic regression, support vector machines, random forests, gaussian mixture
models and discriminant analysis. Each algorithm was evaluated based on classification accuracy, F-1 score and
area under the receiver operating characteristic curve (AUC). The random forest algorithm outperforms other
algorithms with an average accuracy of 90% (F-1 score of 0.91 and AUC score of 0.89). The classification model
can then be used within a ROP optimization model (or framework) to determine optimal operation parameters
which do not result in stick-slip conditions while drilling addressing a serious limitation of previously published
ROP optimization papers. Keywords: Vibrations | Logistic regression | Gaussian mixture models | Linear discriminant analysis | Machine learning |
مقاله انگلیسی |
6 |
الگوریتم جدید ژنتیک برای انتخاب احتمالی در تجزیه و تحلیل امنیتی استاتیک در سیستم های قدرت الکتریکی
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 23 اهمیت یک منبع قابل اعتماد از برق در جامعه صنعتی غیر قابل انکار است. در مراکز کنترل آب و برق حتی شرکت هایی که شبکه های هوشمند مدرن در اختیار دارند، وظیفه مهم تجزیه و تحلیل امنیتی است . در این کار احتمال قطع برق، از عملکرد یک یا چند دستگاه می باشد در حالیکه انتخاب احتمالی تعیین شدیدترین احتمالات بر روی سیستم می باشد. با وجود پیشرفت های فن آوری تجزیه و تحلیل تمام احتمالات ممکن غیر عملی است . در این مقاله، یک روش به منظور انتخاب موثر احتمالات متعدد ارائه شده است . این مسئله به عنوان یک مسئله بهینه سازی ترکیبی مدل سازی شده و توسط الگوریتم های ژنتیک حل شده است که توسعه ای برای این نرم افزار می باشد . یک متد قوی که جریان برق و ولتاژ را در نظر میگیرد ارائه و بر روی سیستم آزمایشی IEEE-30 و همچنین بر روی یک سیستم زندگی واقعی و بزرگ با درنظر گرفتن قطع دو مورد از شاخه ها مورد آزمایش قرار گرفته است .
کلمات کلیدی: سیستم های قدرت الکتریکی، تجزیه و تحلیل امنیت استاتیک، انتخاب احتمالی، الگوریتم های ژنتیک
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مقاله ترجمه شده |
7 |
Enriching semantic knowledge bases for opinion mining in big data applications
غنی سازی پایگاه دانش معنایی برای استخراج افکار در برنامه های کاربردی داده های های بزرگ-2015 This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for
opinion mining with a focus on Web intelligence platforms and other high-throughput big data
applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i)
identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific
training corpus, and (iii) ground this contextual information to structured background knowledge sources
such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when
using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in
conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept
grounding, and on the quality of the enrichment process.
Keywords:
Web intelligence
Social Web
Big data
Knowledge extraction
Opinion mining
Sentiment analysis
Disambiguation
Contextualization
Common-sense knowledge
Concept grounding |
مقاله انگلیسی |