A knowledge-based expert system to assess power plant project cost overrun risks
یک سیستم خبره مبتنی بر دانش برای ارزیابی هزینه ریسک بیش ازحد پروژه نیروگاهی-2019
Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated na- ture of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and un- certainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model ( i.e. a modified Bayesian belief network model) . The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) sig- nificantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complex- ity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor’s poor plan- ning and scheduling.
Keywords: Cost overruns | Risk assessment | Power plant projects | Fuzzy logic | Canonical model
Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment
تصمیم گیری خودکار وسیله نقلیه با استفاده از یادگیری تقویتی عمیق و محیط شبیه سازی با وفاداری بالا-2019
Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve AVs’ ability of environment recognition and vehicle control, while the attention paid to decision making is not enough and the existing decision algorithms are very preliminary. Therefore, a framework of the decisionmaking training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following (CF), is trained within this framework. In addition, theoretical analysis and experiments were conducted to evaluate the proposed reward functions for accelerating training using DRL. The results show that on the premise of driving comfort, the efficiency of the trained AV increases 7.9% and 3.8% respectively compared to the classical adaptive cruise control models, intelligent driver model and constant-time headway policy. Moreover, on a more complex three-lane section, we trained an integrated model combining both CF and lane-changing behavior, with the average speed further growing 2.4%. It indicates that our framework is effective for AV’s decision-making learning.
Keywords: Automated vehicle | Decision making | Deep reinforcement learning | Reward function
Sexually Transmitted Diseases Among US Adolescents and Young Adults
بیماریهای مقاربتی در بین نوجوانان و بزرگسالان آمریکایی-2019
Although sexually transmitted diseases (STDs) affect individuals of all ages, they take a particularly heavy toll on young people. Expanded, integrated, multilevel approaches are warranted to reverse recent increases in STDs and improve sexual and reproductive health outcomes for adolescents and young adults in the United States. Approaches must reach beyond clinics and school classrooms; capitalize on cuttingedge, youth-friendly technologies; and change social contexts in ways that encourage young people’s healthy sexual decision-making.
KEYWORDS : Adolescents | Young adults | Sexually transmitted diseases | Epidemiology | Clinical practice guidelines | Prevention
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
مرور سیستماتیک تشخیص کمک به رایانه در پزشکی: تحولات گذشته و حال-2019
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diag- nostic decision-making process of medical experts, they can be considered as expert systems in medicine. Furthermore, CAD systems in medicine may process clinical data that can be complex and/or massive in size. They do so in order to infer new knowledge from data and use that knowledge to improve their diagnostic performance over time. Therefore, such systems can also be viewed as intelligent systems be- cause they use a feedback mechanism to improve their performance over time. The main aim of the literature survey described in this paper is to provide a comprehensive overview of past and current CAD developments. This survey/review can be of significant value to researchers and professionals in medicine and computer science. There are already some reviews about specific aspects of CAD in medicine. How- ever, this paper focuses on the entire spectrum of the capabilities of CAD systems in medicine. It also identifies the key developments that have led to today’s state-of-the-art in this area. It presents an ex- tensive and systematic literature review of CAD in medicine, based on 251 carefully selected publica- tions. While medicine and computer science have advanced dramatically in recent years, each area has also become profoundly more complex. This paper advocates that in order to further develop and im- prove CAD, it is required to have well-coordinated work among researchers and professionals in these two constituent fields. Finally, this survey helps to highlight areas where there are opportunities to make significant new contributions. This may profoundly impact future research in medicine and in select areas of computer science.
Keywords: Computer-aided diagnosis | Computer-aided detection | Expert and intelligent systems | Computerized signal analysis | Segmentation | Classification
Globally-biased BIRECT algorithm with local accelerators for expensive global optimization
الگوریتم BIRECT مغرضانه جهانی با شتاب دهنده های محلی برای بهینه سازی جهانی ارزشمند-2019
In this paper, black-box global optimization problem with expensive function evaluations is considered. This problem is challenging for numerical methods due to the practical limits on computational budget often required by intelligent systems. For its efficient solution, a new DIRECT-type hybrid technique is proposed. The new algorithm incorporates a novel sampling on diagonals and bisection strategy (instead of a trisection which is commonly used in the existing DIRECT-type algorithms), embedded into the globally-biased framework, and enriched with three different local minimization strategies. The numerical results on a test set of almost 900 problems from the literature and on a real-life application regarding nonlinear regression show that the new approach effectively addresses well-known DIRECT weaknesses, has beneficial effects on the overall performance, and on average, gives significantly better results compared to several DIRECT-type methods widely used in decision-making expert systems.
Keywords: Nonlinear global optimization| DIRECT-type algorithms | BIRECT algorithm | hybrid optimization algorithms | nonlinear regression
Analytic network process: Academic insights and perspectives analysis
فرآیند شبکه تحلیلی: بینش دانشگاهی و تحلیل چشم اندازها-2019
Diversity multi-criteria decision-making methods have been developed to address different complex decision-making problems, and the analytic network process has been found to be one of the most effective techniques. There is an increase in the quality and quantity of publications related to the analytic network process. This detailed overview can provide the research status and development characteristics of analytic network process research and will be useful to researchers for future research directions. To achieve these goals, bibliometric techniques were used. In addition, past and present hotspots of analytic network process research were concluded, and future research trends were determined. The bibliometric analysis was carried out from various aspects including countries and regions, institutions, journals, authors, research areas, articles and author keywords based on data harvested from the Web of Science database. There were 1485 analytic network process-related publications retrieved from theWeb of Science. The results show that Expert Systems with Applications was the most productive journal publishing articles in analytic network process research (118); its number of publications has decreased dramatically since 2013, while Journal of Cleaner Production has shown an upward trend in recent years and ranks second with 47 publications. The most collaborative country is the United States. Taiwan takes a leading position in analytic network process research with 436 publications (29.36%), and National Chiao Tung University, which is located in Taiwan, produced the most articles and has gained the highest h-index (28). The major hot topics that employ analytic network process are sustainability, environmental management and supply chain management. These topics may continue to attract more attention in the future.
Keywords: Analytic Network Process | Web of science | Bibliometrics | Hot topics | Sustainability | Environmental management | Supply chain management
Legal capacity, mental capacity and supported decision-making: Report from a panel event
ظرفیت حقوقی، ظرفیت ذهنی و پشتیبانی تصمیم گیری: گزارش از یک رویداد پانلی-2019
Against a backdrop of the UN Convention on the Rights of Persons with Disabilities having been in place for over a decade, discussions about legal capacity, the relevance of mental capacity and the shift to supported decisionmaking, continue to develop. A panel event was held at the Kings Transnational Law Summit in 2018 with the aim of understanding the contours of the dialogue around these issues. This paper presents the contributions of the panel members, a summary of the discussion that took place and a synthesis of the views expressed. It suggests that divergent conclusions in this area turn on disagreements about: the consequences of sometimes limiting legal capacity for people with mental disabilities; the emphasis placed on particular values; the basis for mental capacity assessments; and the scope for supported decision-making. It also highlights the connection between resources, recognition and freedoms for people with mental disabilities, and therefore the issues that arise when discussion in this area is limited to legal capacity in the context of decision-making.
Keywords: Legal capacity | Mental capacity | Supported decision-making | Mental disability | UN Convention on the Rights of Persons with | Disabilities | Article 12
Psychiatric patients requesting euthanasia: Guidelines for sound clinical and ethical decision making
بیماران روانی درخواست کشتن از سر ترحم: دستورالعمل هایی برای تصمیم گیری بالینی و اخلاقی سالم-2019
Background: Since Belgium legalised euthanasia, the number of performed euthanasia cases for psychological suffering in psychiatric patients has significantly increased, as well as the number of media reports on controversial cases. This has prompted several healthcare organisations and committees to develop policies on the management of these requests. Method: Five recent initiatives that offer guidance on euthanasia requests by psychiatric patients in Flanders were analysed: the protocol of Ghent University Hospital and advisory texts of the Flemish Federation of Psychiatry, the Brothers of Charity, the Belgian Advisory Committee on Bioethics, and Zorgnet-Icuro. These were examined via critical point-by-point reflection, focusing on all legal due care criteria in order to identify: 1) proposed measures to operationalise the evaluation of the legal criteria; 2) suggestions of additional safeguards going beyond these criteria; and 3) remaining fields of tension. Results: The initiatives are well in keeping with the legal requirements but are often more stringent. Additional safeguards that are formulated include the need for at least two positive advices from at least two psychiatrists; an a priori evaluation system; and a two-track approach, focusing simultaneously on the assessment of the patients euthanasia request and on that persons continuing treatment. Although the initiatives are similar in intent, some differences in approach were found, reflecting different ethical stances towards euthanasia and an emphasis on practical clinical assessment versus broad ethical reflection. Conclusions: All initiatives offer useful guidance for the management of euthanasia requests by psychiatric patients. By providing information on, and proper operationalisations of, the legal due care criteria, these initiatives are important instruments to prevent potential abuses. Apart from the additional safeguards suggested, the importance of a decision-making policy that includes many actors (e.g. the patients relatives and other care providers) and of good aftercare for the bereaved are rightly stressed. Shortcomings of the initiatives relate to the aftercare of patients whose euthanasia request is rejected, and to uncertainty regarding the way in which attending physicians should manage negative or conflicting advices, or patients suicide threats in case of refusal. Given the scarcity of data on how thoroughly and uniformly requests are handled in practice, it is unclear to what extent the recommendations made in these guidelines are currently being implemented.
Keywords: Medical assistance in dying | Psychiatry | Mental health | Belgium | Euthanasia | Guidelines
Radiological images and machine learning: Trends, perspectives, and prospects
تصاویر رادیولوژی و یادگیری ماشین: روند، دیدگاه ها، و چشم انداز-2019
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
Keywords: Deep learning | Machine learning | Imaging modalities | Deep neural networ
Developing a hybrid analytics approach to measure the efficiency of deposit banks
تدوین رویکرد تحلیلی ترکیبی برای سنجش کارایی بانکهای سپرده-2019
This study aims at analyzing the efficiency of deposit banks using contemporary analytics-based decision-making techniques within a fuzzy environment. Specifically, a hybrid analytic model drawing on a fuzzy analytical network process and data envelopment analysis was developed and applied to the assessment of Turkish deposit banks quoted on Borsa Istanbul. The findings revealed that; (i) the efficiency results for banking activity vary for competitiveness and for the adoption of new technologies before and after the financial recession; (ii) the majority of deposit banks operating primarily with non-interest based factors found to be less-efficient; (iii) the ownership and capital structure of banks do not significantly contribute to their banking performance, as they were technically inefficient during the same period; and (iv) the inputs of the banking activities could be reduced while a constant level of output is maintained by adopting and properly using the most efficient technology to boost the technical efficiency
Keywords: Fuzzy analytic network process | Data envelopment analysis | Efficiency | Banking performance | Emerging markets |Borsa Istanbul