با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions
ارزیابی ریسک کمی و کیفی پروژه با استفاده از یک مدل توسعه یافته PMBOK تحت شرایط غیر قطعی-2020
This study presented a qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions. Accordingly, an exploratory and applied research design was employed in this study. The research sample included 15 experienced staff working in main and related positions in Neyr Perse Company. After reviewing the literature and the Project Management Body of Knowledge (PMBOK), 32 risk factors were identified and their number reduced to 17 risks using the expert opinions via the fuzzy Delphi technique run through three stages. The results of the confirmatory factor analysis showed that all risks were confirmed by the members of the research sample. Then the identified risks were structured and ranked using fuzzy DEMATEL and fuzzy ANP techniques. The final results of the study showed that the political and economic sanctions had the highest weight followed by foreign investors’ attraction and the lack of regional infrastructure.
Keywords: Project risks | Project management body of knowledge (PMBOK) | Uncertainty | Mixed qualitative and quantitative risk assessment approach | Mathematics | Probability theory | Engineering | Industrial engineering | Business
Optimal planning of distributed photovoltaic generation for the traction power supply system of high-speed railway
برنامه ریزی بهینه از تولید فتوولتائیک توزیع شده برای سیستم منبع تغذیه کششی راه آهن با سرعت بالا-2020
The ever-increasing electricity price and energy consumption in high-speed railway industry push railway companies to seek a promising way to realize their sustainable developments. Making full use of the solar resource along with high-speed railways can be a potential solution to cut the electricity bill, bring more profit to railway companies and realize the decarbonization of high-speed railway industry. This paper studies the optimal planning of distributed photovoltaic generation (DPVG) and energy storage system (ESS) for the traction power supply system (TPSS) of high-speed railway. A quantitative method is proposed to study the time and space characteristics of photovoltaic generation and electricity demand of high-speed trains. An integrated cost-benefit analysis framework is developed to evaluate the effect of DPVG and ESS on the economy of TPSS. To derive the optimal planning scheme and energy management strategy of DPVG and ESS, a mathematical programming model with the objective of minimizing the total cost is proposed to seek the most economical solution. A hybrid global optimal solution approach is developed to solve the model. A real-world case of Beijing-Baoding high-speed railway in China is used to illustrate the capability and characteristics of the proposed model. The computational results show that DPVG is able to supply 32:5% electricity demand of high-speed trains. The integration of DPVG and ESS can help railway company save 4.2 million CNY each year in Beijing- Baoding high-speed railway. This paper demonstrates the potential and applicability of DPVG and ESS in high-speed railway industry.
Keywords: High-speed railway | Photovoltaic generation | Energy storage system | Traction power supply system
Biomedical and Clinical Research Data Management
مدیریت داده های تحقیقات زیست پزشکی و بالینی-2020
Systems medicine describes an interdisciplinary approach in medicine with the aim of improving disease prevention, diagnosis, targeted treatment, and prognosis (Apweiler et al., 2018). Often, statistical, mathematical, and computational concepts of systems biology are translated to systems medicine for clinical use (Bauer et al., 2017). This approach typically requires large amounts of structured clinical and biomedical data covering the respective disease and patients (Gietzelt et al., 2016a). Thus, it is important to have efficient procedures and policies in place to prepare the data from different data sources. For research projects in systems medicine, some of the data are generated as part of the project, while others were generated beforehand, often for other purposes, e.g. in clinical routine and in different legal context. Therefore, one of the first steps of a project is to ensure the availability of the data needed. This task not only includes the process of assembling data files from various sources. After retrieval of the data, they typically have to be checked and filtered for their quality (Huebner et al., 2016), converted into an appropriate format, pre-processed, and harmonized into common formats that reflect standardized data definitions and ontologies (Krishnankutty et al., 2012). In addition, biomedical research often relies on patient-related data, requiring additional steps like checking the permission to use the data for the intended purpose or de-identifying the data—usually based on informed consent or special legislation. While these steps seem to be straightforward and common to most research projects, many projects re-implement customized solutions to build up their own infrastructure and cope with the data management challenges. Since projects usually focus on a biomedical research question, the effort of data preparation, harmonization, and management is often underestimated and scientists with a data or computer science background are often invited to the project only at a later stage.
Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing building
هماهنگی منابع ظرفیتی تجدید پذیر وسایل نقلیه به خانه برای مدیریت انرژی در ساختمان انعطاف پذیر و خود شفایی-2020
The home energy management is an efficient tool to manage energy in the buildings that organizes different technologies and mathematical techniques to minimize energy cost. Home energy management often utilizes renewable energy resources to supply load demand in the building. Current home energy management systems utilize one or several of the available hardware-software capacity resources to deal with energy consumption in the buildings. However, a comprehensive model including various hardware and software capacity resources may increase the flexibility of the model. In this regard, this paper studies an efficient paradigm for home energy management in the building connected to electric grid. The proposed model forms an energy hub including the hardware resources (i.e., vehicle-to-home, wind turbine, and diesel generator) and software tools (i.e., demand response program). All the capacity resources and grid power are optimally adjusted to minimize the daily operational cost of the building as well as improvement of resiliency and self-healing. Wind energy and load uncertainty are modeled through stochastic programming. The seasonal pattern is considered for loads, prices, and wind energy. Simulation results demonstrate that operating all capacity resources minimizes the daily operational cost. When the wind energy, demand response program, vehicle-to-home, and diesel generator are not utilized, the cost is increased by 900, 230, 84, and 322%, respectively. It is also confirmed that the building not only can operate when one of the components is not connected, but also it is able to supply the demand under off-grid operation.
Keywords: Demand response program | Home energy management | Resiliency | Stochastic mixed integer binary model | Vehicle to home | Wind turbine
Optimal process design for integrated municipal waste management with energy recovery in Argentina
طراحی فرآیند بهینه برای مدیریت یکپارچه زباله شهری با بازیابی انرژی در آرژانتین-2020
This work presents a comprehensive mathematical model for the optimal selection of municipal waste treatment alternatives, accounting for co-digestion of sludge and municipal solid waste. The superstructure of alternatives includes anaerobic digestion under mesophilic or thermophilic conditions, composting, recycling, and final disposal in a landfill. Anaerobic digesters can be fed with different mixing ratios of sewage sludge (SS) and the organic fraction of municipal solid waste (OF). A mixedinteger mathematical programming formulation is proposed to find the optimal process design. It comprises nonlinear equations to estimate digestion yields according to substrate mixing ratios. Results for cities of different sizes show that the joint treatment can increase profitability, especially in small populations. In all cases, co-digestion of the full stream of SS and OF leads to an integrated waste-toenergy process that maximizes the economic value and reduces environmental impacts of waste by producing electricity, heat and fertilizer.
Keywords: Co-digestion | Waste-to-Energy | Optimization | Superstructure | Process design
An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview
اینترنت چارچوب انرژی با منابع انرژی توزیع شده ، پیشرانها و نیروگاه های مجازی در مقیاس کوچک: یک مرور کلی-2020
Current power networks and consumers are undergoing a fundamental shift in the way traditional energy systems were designed and managed. The bidirectional peer-to-peer (P–P) energy transactions pushed passive consumers to be prosumers. The future smart grid or the internet of energy (IoE) will facilitate the coordination of all types of prosumers to form virtual power plants (VPP). The paper aims to contribute to this growing area of research by accumulating and summarizing the significant ideas of the integration of distributed prosumers and small-scale VPP to the internet of energy (IoE). The study also reports the characteristics of IoE in comparison to the traditional grid and offers some valuable insights into the control, management and optimization strategies of prosumers, distributed energy resources (DERs) and VPP. As bidirectional P–P energy transaction by the prosumers is a crucial element of IoE, their management strategies including various demand-response approach at the customers’-levels are systematically summarized. The integration of DERs and prosumers to the VPP considering their functions, infrastructure, type, control objectives are also reviewed and summarized. Various optimization techniques and algorithm, and their objectives functions and the types of mathematical formulation that are used to manage the DERs and VPP are discussed and categorized systematically. Finally, the factors which affect the integration of DERs and prosumers to the VPP are identified.
Keywords: Bidirectional energy transactions | Distributed energy resources | Energy management | Internet of energy | Optimization techniques | Prosumers | Virtual power plant
Computational analysis of NIRS and BOLD signal from neurovascular coupling with three neuron-system feedforward inhibition network
تجزیه و تحلیل محاسباتی سیگنال های NIRS و BOLD از اتصال جفت عصبی عروقی با سه شبکه مهار کننده تغذیه ای سیستم عصبی-2020
Several neurological disorders occur due to hypoxic condition in brain arising from impairment of cere- bral functionality, which can be controlled by neural stimulation driven vasoactive response mediated through biological response in astrocyte, a phenomenon known as neurovascular coupling. Brain can ad- just with the problem of hypoxic condition by causing vasodilation with the help of this mechanism. To deduce the mechanism behind vasodilation of blood vessel caused by neuronal stimulus, current study articulates a mathematical model involving neuronal system feedforward inhibition network model (FFI) with two other functional components of neurovascular coupling, i.e. astrocyte and smooth muscle cell lining blood vessel. This study includes the neural inhibition network system where glutamatergic pyra- midal neuron and GABAergic interneuron act antagonistically with each other. The proposed model suc- cessfully includes the implication of the inhibition system to design mathematical model for neurovas- cular coupling. Result of the proposed model shows that the increase in neuronal stimulus from 20 to 60 μA/cm 2 has the ability to increase the vasodilatory activity of blood tissue vasculature. Oxygenation level and hemodynamic response due to input synaptic stimulation has been calculated by regional cere- bral oxygenation level (rS0 2 ) and blood oxygen level dependent (BOLD) imaging signal which supports vasodilation of blood vessel with increase in synaptic input stimulus.
Keywords: Neurovascular coupling unit | Hodgkin-Huxley model | Neurotransmitter | Feedforward-inhibition network | Regional cerebral oxygen saturation
Data-driven switching modeling for MPC using Regression Trees and Random Forests
مدل سازی سوئیچینگ داده محور برای MPC با استفاده از درختان رگرسیون و جنگل های تصادفی-2020
Model Predictive Control is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict a system’s behavior over a time horizon. However, building physics-based models for complex large-scale systems can be cost and time prohibitive. To overcome this problem we propose a methodology to exploit machine learning techniques (i.e. Regression Trees and Random Forests) in order to build a Switching Affine dynamical model (deterministic and Markovian) of a large-scale system using historical data, and apply Model Predictive Control. A comparison with an optimal benchmark and related techniques is provided on an energy management system to validate the performance of the proposed methodology.
Keywords: Regression Trees | Random Forests | Model predictive control | Switching systems | Markov Jump Systems
Influences of electromagnetic radiation distribution on chaotic dynamics of a neural network
تأثیر توزیع تابش الکترومغناطیسی بر پویایی بی نظمی یک شبکه عصبی-2020
Electromagnetic radiation has an effect on the functional behavior of nervous system, and appropriate electromagnetic radiation is helpful to treat some neurological diseases. In this article, we investigate the effects of electromagnetic radiation distribution on the non- linear dynamics of a neural network with n neurons. A new mathematical model of the neural network under electromagnetic radiation is developed and analyzed, where electro- magnetic radiation is equivalent to the magnetic flux passing through the cell membrane. Chaotic dynamics of the nerve system is detailedly studied by stimulating different num- ber of neurons in the neural network model consisted of three neurons. It is proved that with the increasing of the number of neurons stimulated by electromagnetic radiation, the dynamics behaviors of the neural network gradually change from period moving to chaos, transient chaos and intricate hyperchaos. That is, the dynamical behaviors of the neural system can be modulated through changing the number of neurons affected by electro- magnetic radiation in neural network. Therefore, it could give new insights to understand the occurrence mechanism of some neuronal diseases. Moreover, a flexible hardware cir- cuit of the neural network with different electromagnetic radiation distribution is imple- mented by using commercially available electronic elements, and the experimental mea- surements are consistent with numerical simulation results
Keywords: Neural network | Dynamics behaviors | Transient chaos | Hyperchaos | Electromagnetic radiation
Graph-theoretical derivation of brain structural connectivity
استخراج نمودار نظری از اتصال ساختاری مغز-2020
Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense ef- forts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from exper- imental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric con- nections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system.
Keywords: Connectome | Neuronal networks | Random graphs