Plating, Electroless Nickel, Codeposited with Polytetrafluoroethylene (PTFE)

ممنون میشم مقالات مجله Journal of Environmental Psychology

برای سالهای 2008 تا 2013 برام بفرستید .

برای مقاله نویسی به شدت احتیاج دارم .

کسی میتونه این مقاله رو برام بگیره؟

 http://link.springer.com/content/pdf/10.1007%2FBF01076005

ممنون

میشه این مقاله رو برام دانلود کنید:

Blind biosignal classification framework based on DTW algorithm

اینم لینکشه

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6017023&contentType=Conference+Publications&queryText%3DBlind+biosignal+classification+framework+based+on+DTW+algorithm

میشه سزیع اینکارو برام بکنید؟ خیلی ممنون

International Journal for Numerical Methods in Engineering
Volume 35, Issue 2, pages 307–324, 15 August 1992
 

http://onlinelibrary.wiley.com/doi/10.1002/nme.1620350206/abstract

اگه امکان داره مقاله زیر رو برام بذارید

title: Protocol Stack for Nano Networks

Computer, Consumer and Control (IS3C), 2012 International Symposium on

Author(s): Shirazi, A.Z.

یکی از دوستان لطف کنه مقالات زیر رو که لینکشون رو میزارم دانلود کنه. نیاز خیلی فوری دارم.

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=557785&contentType=Conference+Publications&queryText%3DFeasibility+analysis+of+fault-tolerant+real-time+task+sets

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1234530&contentType=Journals+%26+Magazines&queryText%3DAn+optimal+fixed-priority+assignment+algorithm+for+supporting+fault-tolerant+hard+real-time+systems

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1425034&contentType=Conference+Publications&queryText%3DA+fault-tolerant+scheduling+scheme+for+hybrid+tasks+in+distributed+real-time+systems

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1300428&contentType=Conference+Publications&queryText%3DA+fault-tolerant+scheduling+scheme+for+hybrid+tasks+in+distributed+real-time+systems

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4296862&contentType=Conference+Publications&queryText%3DTERCOS:+A+Novel+Technique+for+Exploiting+Redundancies+in+Fault-Tolerant+and+Real-Time+Distributed+Systems

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5192227&contentType=Conference+Publications&queryText%3DA+novel+fault-tolerant+scheduling+algorithm+for+periodic+tasks+of+Distributed+control+systems

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5633237&contentType=Conference+Publications&queryText%3DFault-tolerant+task+scheduling+in+multiprocessor+systems+based+on+primary-backup+scheme

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1541084&contentType=Conference+Publications&queryText%3DFault+tolerant+scheduling+for+fixed-priority+tasks+with+preemption+threshold

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4020794&contentType=Conference+Publications&queryText%3DScheduling+Hard-Real-Time+Tasks+with+Backup+Phasing+Delay

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4041915&contentType=Conference+Publications&queryText%3DFault-Tolerant+Partitioning+Scheduling+Algorithms+in+Real-Time+Multiprocessor+Systems

با تشکر

http://www.springerlink.com/content/ujm76138r5323174/

http://www.springerlink.com/content/j7781077288hh565/

خیلی ممنون.

حکایت دست من و دامان شماست

اگه دسترسی دارید ممنون میشم

فوری فوریه اینم لینکش:

http://www.sciencedirect.com/science/article/pii/0022247X85903531

خیلی ممنون میشم اگه کمک کنید

عنوان مقاله :Fault Tree Analysis, Methods, and Applications 

نویسندگان :  

 Author(s): Lee, W. S.
Dept. of Industrial Engineering; Durland Hall; Kansas State University; Manhattan, Kansas 66506 USA.
Grosh, D. L. ; Tillman, F. A. ; Lie, C. H.

تاریخ و محل انتشار: 

 This paper appears in:
Reliability, IEEE Transactions on
Date of Publication: Aug. 1985

http://www.scientific.net/AMR.550-553.1039

Advanced Materials Research (Volumes 550 - 553)
Volume Advances in Chemical Engineering II
Edited by Zili Liu, Feng Peng and Xiaoguo Liu
Pages 1039-1042
DOI 10.4028/www.scientific.net/AMR.550-553.1039
Citation Gui Rong Cao et al., 2012, Advanced Materials Research, 550-553, 1039
Online since July, 2012

Chen Shi-quan

Department of Mathematics, Guizhou Normal University, Guiyang, Guizhou, China

Available online 20 May 2003.

http://dx.doi.org/10.1016/0165-0114(88)90099-1, How to Cite or Link Using DO

http://www.sciencedirect.com/science/article/pii/0165011488900991

موضوع : 1332-1998 - IEEE Standard Reliability Program for the Development and Production of Electronic Systems and Equipment

مشخصات : Date of Publication: 1998

Product Type: Standards

لینک دانلود : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=720572&contentType=Standards&queryText%3Dieee+1332
 

عنوان مقاله :  Development of reliability program standard GEIA-STD-0009

مشخصات مقاله : This paper appears in:
Reliability and Maintainability Symposium, 2009. RAMS 2009. Annual
Date of Conference: 26-29 Jan. 2009
Author(s): Cushing, M.J.
US Army Evaluation Center, Attn: TEAE-RE, Aberdeen Proving Ground, MD
Long, E.A. ; Hockenberry, M.
Page(s): 298 - 303
Product Type: Conference Publications

لینک دانلود : http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4914692&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4914692

Reliability Program Standard

Product Code: JA1000
Date Published: 2012-05-07
Date Reaffirmed: May 2012

Issuing Committee:
G-11r, Reliability Committee
 

لینک دانلود :  http://standards.sae.org/ja1000_201205

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6122530&url=http://ieeexplore.ieee.org/xpls/abs_all.jsp%3Farnumber%3D6122530

A wireless wearable ECG sensor for long-term applications
Date of Publication: January 2012
Author(s): Nemati, E.
McMaster Univ., Hamilton, ON, Canada

متشکرم

در واقع من یک موضوع کلی مد نظرم هست که از دوستان میخوام در پیدا کردن نام مقاله های معتبر بین المللی که به این موضوع مربوط میشه کمکم کنن تا بتونم اون مقاله ها رو از این سایت خریداری کنم و اطلاعات مورد نظرمو از داخل این مقالات کسب بکنم. با تشکر و سپاس فراوان از همه

موضوع : Equipment Needed for Recovery and Processing of Steel Slag
 

مقاله زیر مورد نیاز در صورت دسترسی لطفاً ارسال بفرمایید

با سپاس

Vrentzos, G. E., J. A. Papadakis, et al. (2003). "Intravenous administration of vitamin B12 in the treatment of hyperhomocysteinemia associated with end-stage renal disease." J Nephrol 16(4): 535-539

به مقاله زیر نیاز است. سپاسگزار خواهم بود در صورت دسترسی ارسال بفرمایید.

Trimarchi, H., A. Schiel, et al. (2002). "Randomized trial of methylcobalamin and folate effects on homocysteine in hemodialysis patients." Nephron 91(1): 58-63.

نویسنده : Young-Ju Han, Min-Woo Park and Tai-Myoung Chung

ناشر و ... : Lecture Notes in Computer Science, 2010, Volume 6018, Computational Science and Its Applications – ICCSA 2010, Pages 142-157

لینک : http://www.springerlink.com/content/h32r7250081j6065/?MUD=MP
 

نویسندگان: Daniel R. Dreyer , Shanthi Murali , Yanwu Zhu , Rodney S. Ruoff and Christopher W. Bielawski

مجله: Journal of Materials Chemistry

سال : 2011

DOI: 10.1039/C0JM02704A

آدرس: http://pubs.rsc.org/en/content/articlelanding/2011/jm/c0jm02704a

لینک دانلود مقاله شما به صورت رایگان

برای حمایت از ما بر روی علامت گوگل + (Google + ) در سمت چپ صفحه کلیک کنید و یا با لینک کردن ما در سایت و یا وبلاگ  خود مقالات بیشتری را به رایگان از ما دریافت کنید. همچنین با معرفی ما به دوستان خود ، مقاله رایگان برای شما ارسال می گردد.

ببخشید من این مقاله رو از قسمت حسابدار ی سایت می خواستم واینم ایمیلمه m13682@yahoo.com  باتشکر

Quality of reported earnings by Chinese firms: The influence of ownership structure
Advances in Accounting, Volume 28, Issue 1, June 2012, Pages 193-199

Wei Xu, Kun Wang, Asokan Anandarajan                                             

                                                                                       واقعا ازتون ممنونم اگه میشه به ایمیلم ارسال کنید

                                                                                                             ببخشید

لینک دانلود مقاله شما به صورت رایگان

برای حمایت از ما بر روی علامت گوگل + (Google + ) در سمت چپ صفحه کلیک کنید و یا با لینک کردن ما در سایت و یا وبلاگ  خود مقالات بیشتری را به رایگان از ما دریافت کنید. همچنین با معرفی ما به دوستان خود ، مقاله رایگان برای شما ارسال می گردد.

Fuzzyleastsquares

Phil Diamond∗

برای دانلود مابقی مقالات از سرویس های ما استفاده کنید و یا با لینک کردن سایت ما، مقاله رایگان دریافت کنید

Ali Haj-fraj and Fredrich Pfeiffer, "Dynamic Modeling and Analysis of Automatic Transmission" , IEEE/ASME International Confrence on Advanced Intelligent Mechatronics. 1999 Atlanta, USA.

Hayashi K., Shimizu Y., Nakamura S., Dote Y., Takayarha A. and Hirako, 1993, A Neuro Fuzzy Optimal Transmission Control for Automobile with Variable Loads, IEEE.

با تشکر از همکاری شما دوستان

A multi-agent based framework for supply chain risk management

• Mihalis Giannakis^{a, ,} ,
• Michalis Louis^b

• ^a Operations Management Group, Warwick Business School, The University of Warwick, Coventry CV4 7AL, UK
• ^b The University of Warwick, Coventry CV4 7AL, UK

• Received 10 September 2009. Revised 30 April 2010. Accepted 5 May 2010. Available online 14 May 2010.

• http://dx.doi.org/10.1016/j.pursup.2010.05.001, How to Cite or Link Using DOI
• Cited by in Scopus (7)

• Permissions & Reprints

Keywords

• Multi-agent systems;
• Supply chain management;
• Risk management

1. Introduction

The increasing call for mass customization in many industries has made today’s global supply chains very complex, requiring a multitude of parallel information and physical flows to be controlled to ensure high customer service levels. This increased complexity raises the level of uncertainty and risks that companies are faced with Manuj and Mentzer (2008). The wide range of risks along the supply chain (both from supply and demand side) may impose negative implications upon supply chain performance. There is an eminent need for organizations to have necessary strategies to manage these risks and disruptions, so that they can achieve the necessary level of agility for effective mass customization.

Constructive collaboration among business partners in supply chains is vital in any attempt to mitigate risks and ameliorate disruptions, to achieve responsiveness and to offer a high customer service level (Hallikas et al., 2004). Many successful modern organizations have shifted from an opportunistic dogma of cooperation to a synergetic ethos of collaboration and aligned their supply chain processes.

The use of Information and Communication Technology (ICT) tools is perceived as a paramount facilitator for the realisation of this collaborative perception, offering the capabilities of information sharing, customer sensitivity and process integration (Wu and Angelis, 2007). The conventional IT, however, (which is based on legacy systems) has not provided sustainable solutions for collaborative Supply Chain Management (SCM). It lacks real-time adaptability in supply chains and focuses on dyadic contexts of collaboration rather than collaboration amongst a plethora of partners (Akkermans et al., 2003). It is also characterized by inflexibility for the reconfiguration of supply chains processes and high development and maintenance costs (Botta–Genoulaz et al., 2005).

The use of multi-agent modelling (a sub category of artificial intelligence) can be an alternative decision making tool for collaboration within supply chains. In computer science, an agent can be defined as a software entity, which is autonomous to accomplish its design objectives, considered as a part of an overall objective, through the axiom of communication and coordination with other agents (Gilbert, 2007). Through this paradigm of software architecture, the management of supply chain processes can be perceived as facilitated by several autonomous decision making entities (software agents), each responsible for specific activities and performing different roles. These agents interact and cooperate with other agents, within and across organizations, in order to solve problems beyond their individual knowledge or expertise, and to promote a higher performance for the entire system (Stone and Veloso, 2000).

In this paper, a multi-agent based framework is proposed as the conceptual basis for the design of a DSS that facilitates collaborative disruption risk management in manufacturing supply chains. The framework supports the fulfilment of production, event and disruption risk management constituted by coordination, communication and task agents and draws on principles and theories of SCM, agent based simulation and computer science.

The remainder of the paper is organised in four sections. In the first section, the usefulness of a multi-agent system (MAS) framework for supply chain risk management (SCRM) is discussed through a brief review of an expansive SCRM literature, a comparison between conventional IT solutions and MAS and a discussion of the application of software agents to different supply chain problems. The second section presents the analytical approach that has been utilized, the process for the development of the framework and its features in detail. With the use of a hypothesized scenario, the third section presents the processes for supply chain disruption management that an MAS designed with the logical structure of the proposed framework will follow. The paper concludes with a discussion of the limitations and managerial implications of the framework and potential extension of the research.

2. Literature review

The SCM literature is rife of studies that investigate supply chain risk phenomena and provide models for the analysis and mitigation of several types of supply chain risks (financial, operational and strategic) that occur both in the supply and demand side of supply chains. Zsidisin and Ritchie (2008) and Juttner (2005) provide comprehensive reviews of the literature and models used for an effective SCRM. Several authors have also proposed disruption management strategies for SCRM. Zsidisin and Smith (2005) discuss how early supplier involvement can reduce the likelihood of supply disruptions and Chopra and Sodhi (2004) highlight mitigation strategies for different types of risks, which manufacturing organizations apply to deal with uncertainty. They identify drivers for a wide variety of different risks and pinpoint alternative proactive mitigation strategies for each corresponding risk. Other researchers have focused specifically on information systems risks. Kleindorfer and Saad (2005) underline the importance of collaborative information sharing as the vehicle to shed light upon vulnerabilities within the supply chain, to manage disruptions under a cost efficient manner and to devise strategies for their effective control in a crisis situation.

The existing studies that are found in the literature have been instrumental in identifying and analysing several causes of disruptions and risks in supply chains. The majority of them, however, are based on case studies and empirical evidence, which limits their generalizability beyond the specific contexts and the extension of the strategy beyond dyadic relationships. Melnyk et al. (2009) provide an innovative formalized methodology in mitigating disruptions in supply chains with the use of discrete event simulation. Their approach extends previous studies; however, their study is limited by the lack of real-time adaptability of their model that is evidenced in supply chains. Methodologies that can overcome this limitation like agent based modelling are considered very useful in this respect.

The agent based technology is acknowledged as one of the most promising technologies for effective management of supply chains that are characterized by high levels of uncertainty ( [Brooks and Davenport, 2004] and [Lou et al., 2004]) due to its vital characteristics of:

•

Autonomy: agents are aware of their environment operating without human intervention to some extent in order to fulfil their objectives (Jennings and Wooldridge, 1995)

•

Social ability: an agent can interact with other agents or humans through the use of an agent communication language (Moyaux and Chaib-draa, 2006).

•

Reactivity: agents can perceive their environment and respond to specific changes in this environment (Parunak, 1999)

•

Pro-activeness: agents do not simply respond to changes in their environment, but can initiate actions (Moyaux and Chaib-draa, 2006).

Despite the fact that the technology is relatively old and well known, in today’s business reality multi-agent software platforms are not used widely for the management of business processes. Electronic resource planning systems (ERP) are still the most common software selection for managing a company’s processes (Burka et al., 2005). The latest advancements of ERP systems that incorporate the integration of SCM processes give rise to promises that they can lead to significant improvements (Kelle and Akbulut, 2005).

The efficacy of conventional ERP systems, however, to provide real time synchronization among supply chain partners – which is necessary for effective SCRM – is limited. Their design has been based on the need for integration with legacy systems. This restricts their ability to meet the high level of agility required for decentralized control (Karwowski et al., 2007). The inflexible nature of ERP systems to adapt to rapidly changing business environments in a cost efficient manner, has forced many organizations to fit their processes according to the capabilities of the ERP system in order to save funds and time (Helo et al., 2005).

The latest generation of ERP systems that utilize Service Oriented Architecture and web services for process management has the potential of providing real time information on several activities in an enterprise (Tarantilis et al., 2008), which can speed up the decision making process and lead to better integration amongst supply chain partners. Their modular design increases their flexibility, making them more applicable to several managerial processes. Their computational capability for managing very complex processes, however, is limited (Helo et al., 2005), and their role is confined only in the timely flow of information about the condition of several processes. A considerable amount of time and funds is also required in order to yield benefits from the latest ERP technologies, implying that only organizations that can afford the required high level of investment will be able to achieve responsiveness, and overall to reinforce their competitive advantage. The multi-agent architecture for an SCM (Fig. 1) offers an opportunity to overcome the shortcomings of conventional ERP technology, providing a plethora of advantages.

Through their learning capability, an MAS can demonstrate efficiently the proactive and autonomous behaviour of the participating agents in mitigating risks and rectifying supply chain disruptions in real time ( [Kwon et al., 2007] and [Lu and Wang, 2007]). They can also promote a high level of cross organizational collaboration in a computational and cost efficient manner (Swaminathan et al., 1998).

The inherent distributed nature of agent based technology (in that, a problem solution is distributed to different agents) gives the significant advantage of ease in dealing with the high level of complexity of global supply chains in contrast with conventional information technology (Akkermans et al., 2003). This is enhanced by the fact that each of the agents has a specific expertise and a computational efficiency in dealing with this complexity in combination with easiness of development in a short time frame (Lu and Wang, 2007). With this approach, re-configuration of the whole supply chain can become a reality in a timely fashion and with a low cost.

These benefits can be materialised by incorporating existing legacy systems. An expert system for inventory management, or an ERP system, for example, can be integrated with multi-agent software. The main technique applied is to “wrap” around the legacy code with an agent based software, without the need to rewrite the whole legacy code, in order to “agentify” it into a normal agent (Genesereth and Ketchpel, 1994). On this basis, the wrapping software is used as a “facilitator” to interpret messages from an agent to legacy software and vice versa, aiming at providing an understandable communication platform for both sides (Davidsson and Wernstedt, 2002).

MAS have been used in modelling a multitude of supply chain phenomena: for the identification of negotiation problems in supply chains (Chen et al., 2004), in production and control processes (Caridi and Cavalieri, 2004) in distribution (Swaminathan et al., 1998), and for inventory and demand forecasting (for a thorough literature review of an MAS application on an SCM see Beamon, 1998). Agent based technology has also been utilised for the management of disruptions within a supply chain in some studies. Kimbrough et al. (2002), for example, use it for the reduction of the bullwhip effect through modelling a supply chain with agents. Kwon et al. (2007) propose an agent based framework based on experimental design that deals with supply and demand uncertainty and Bansal et al. (2005) provide a generalized collaborative framework for disruption management oriented to the reality of refinery supply chains.

The papers that deal with the application of an MAS in disruption management focus on particular supply chain risks and/or contexts, but do not explore the learning process of the agent based models to ameliorate abnormalities in supply chain processes and to integrate decisions across the supply chain. Their approach to SCRM is limited as they do not provide the basis for an integrated SCM framework for decision making.

In this paper, these previous works are synthesised and extended through the development of a framework for supply chain disruption management that deals with amelioration of abnormalities in manufacturing supply chains that incorporates basic risk management theory. The proposed framework is enriched with case-based reasoning (decisions for previous cases can be utilized as instances for current decision making), in order to enable the learning capability of the agent (Leak, 1994), by taking into consideration integration issues, so that it can be used as an add-on module to legacy systems.

3. Framework for a disruption management system

For the development of the framework, an analytical sequential approach was adopted. First the organizational design of the framework was formulated and embedded in an overarching agent based SCM model (an artificial society constituted by software agents). Then the roles for each of the agents within the disruption management framework were defined and a detailed description of the responsibilities for each of these roles was provided. The interactions among these agents were subsequently modelled by analyzing several risk identification and mitigation processes.

In Fig. 2, the basic components of a generic multi-agent based model for an SCM are presented. This model is constituted by three modules: (a) production fulfilment processes (e.g. order management, manufacturing, procurement, logistics); (b) supply chain event management; and (c) disruption risk management processes.

The production fulfilment module coordinates the activities of different supply chain partners for the fulfilment of orders through the supply, production and delivery processes. The supply chain event management module is responsible for monitoring the actual fulfilment of specific orders along the supply chain. The role of the disruption risk management module is to initiate the necessary coordination among the agents, when a risk through a potential disruption is identified, related to a specific order or to the overall operational performance.

The focus of this paper is on the disruption risk management module. This module is initiated by the monitoring process of the MAS and incorporates several sequential processes that are described in this section. It synthesizes the monitoring of supply chain events with the disruption management processes. It is comprised of five software agents:

•

Communication agent: responsible for the effective communication between the agents of supply chain partners. In common terms, it facilitates the flow of information among the partners of a supply chain. For example, a logistics service provider (LSP) and a manufacturer can communicate through the facilitation of this agent.

•

Coordination agent: responsible for coordinating the agents of the module within the limits of an organization. The coordination agent of a focal company (e.g. an OEM) orchestrates the disruption management process only for the processes of this company.

•

Monitoring agent: responsible for providing the required monitoring information, by gathering and analyzing corresponding data from all the collaborating parties. It has the ability to trigger an alarm, when an abnormal situation occurs. In effect, it provides inter-organizational visibility regarding either the normality or abnormality of processes related to the fulfilment of an order. For instance, from a supplier’s perspective, it provides supervision of the production process, by monitoring the actual production and comparing it to the production schedule.

•

Wrapper agents: these agents can offer information integration among legacy and agent software. For example, an expert system for inventory management or an ERP system can be integrated with agent software. The main technique applied is to “wrap” around the legacy code with agent software, so that to “agentify” it into a normal agent. On this basis, the wrapping software is used as a “facilitator” for the interpretation of messages from agent to legacy software and vice versa, aiming to provide an understandable communication for both sides (Davidsson and Wernstedt, 2002).

•

Disruption manager agent: it is the main cell of the disruption management module. It is constituted by a rectification proposer (a software agent responsible for the suggestion of corrective actions), a built-in simulator that accounts for the risk assessment and the optimization processes, and a learning component that leverages successful past decisions as cases for future use in similar situations.

The competences and activity limits of the aforementioned agents are summarized in Table 1.

The disruption risk management process is triggered when an unusual event is identified during the order fulfilment process (Fig. 2). The process can be described as follows: after the acceptance by the customer of the proposed delivery time and price, the coordination agent triggers a monitoring process for the specific order. This entails the creation of a monitoring agent devoted to this order (step 1). Consecutively, in a cascading pattern, monitoring agents are generated by the local coordination agents of the supply chain partners, which are directly linked with the specific order (first and second tier suppliers). In this respect, for each supply chain partner that is related to the specific order, a monitoring agent is triggered by the local coordination agent. A recursive process of information gathering then begins (step 2). In the case that an abnormal event is identified within this process, as a cause of a potential disruption, the disruption risk management module is activated (step 3).

Assume, for example, that an abnormal event is identified in the manufacturing process of the second tier supplier by the corresponding monitoring agent. This may be an unusual fluctuation in the manufacturing process, either due to a labor strike, or a problem with manufacturing equipment. The monitoring agent that has identified the abnormal event alerts the local disruption risk management agent, so that the necessary guidelines will be provided, for the proactive mitigation of the situation. The disruption risk management process has begun. This process is illustrated in Fig. 3. A detailed description of the process for risk management and mitigation is presented in the next section.

3.1. Risk management process by the disruption manager

The risk management process is executed in four stages: risk identification, risk assessment, decision and implementation of risk management actions and optimization (Hallikas et al., 2004). The process involves collaboration amongst supply chain partners through exchange of information and allocation of specific roles, in order to enable mutual risk management.

3.1.1. Risk identification

This is the fundamental stage of the entire risk management process, where risks are identified through the use of quantitative models. The backbone of this process is based upon the monitoring of various key performance indicators (KPIs) related to the performance of supply chain partners (e.g. suppliers, LSP). The level of an in-stock inventory, production throughput, capacity utilization and delivery lead times are some of the KPIs that can be used to identify an abnormal situation that may involve a potential risk.

The actual values of those KPIs are monitored within a specific time frame and compared with predefined values that are described either in an agreement among partners, or unofficially when the type of relationship does not require an agreement. Statistical tests can identify significant deviations between the actual and the pre-defined values (Fig. 4). In case a significant deviation is identified, an alarm is triggered by the monitoring agent.

3.1.2. Risk assessment

This stage is necessary for the selection of suitable corrective management actions for the risk factors identified in the previous stage. The risk analysis takes into consideration a wide range of criteria such as the probability of occurrence of the event, the risk level and risk impact, and it prioritizes the risks according to the outcome of this process.

In conjunction with the outcome of the risk assessment, a description for the level of the impact using ordinal scales (e.g. no impact, minor impact, medium impact or serious impact) and the level of probability for the occurrence of the event (e.g. very unlikely, improbable event, moderate event, probable event, very probable ) can be given. This process is executed by a root cause identifier software, which is incorporated in the learning module of the disruption management agent. Through the monitoring of crucial KPI’s (e.g. delivery time, production output), the potential causes of the triggered alarm can be identified. For instance, in case of a significant delivery delay, the root cause identifier will initiate a process to trace the cause of this delay. In the case of inability on behalf of the LSP to deliver the order, the root cause identifier will “label” this incident as probable delays risk.

Subsequently, each of the potential remedies for any identified cause (that is about to become a risk), is evaluated using a built-in simulator. The success of a past decision applied to a specific risk is taken into consideration during the evaluation process. The best past responses to risks are selected, through the existence of a “risk portfolio”, improving the quality of the solutions. This utilization of previous successful knowledge to similar future situations is accomplished through the use of case-based reasoning (Leake, 1994). This system-learning methodology can be applied to the proposed framework through the structure depicted in Table 2.

For instance, in case an alarm is triggered at the side of a supplier due to the identification of an abnormal KPI, all the potential risks that arise (e.g. potential delays) are identified by the root identifier in the field “type of risk”. Potential remedies are then evaluated by the built-in simulator initially in terms of their feasibility and based on specific constraints (e.g. contractual agreements). Then, the expected costs of those feasible scenarios are calculated, generating a list of remedies for the risk that emerges along with the associated costs.

The mechanism that the built-in simulator adopts during the risk assessment process can be described using the model of Shavell (1984) for risk mitigation. With this model the level of the danger, the cost for its mitigation and the potential financial loss that leads to a disruption, are quantified. The following calculations are conducted hierarchically: (a) the probability P(y) for the disruption to become reality is estimated through the use of Failure Mode and Effect Analysis (FMEA) and/or formal mathematical models that can utilise linear regression, time series regression models and stochastic models (Dani, 2009); (b) the amount of financial loss L(y) for the specific disruption to become reality; (c) the investment cost y to mitigate the probability P(y), in order to lessen the specific disruption risk; (d) an optimal y^⁎ investment cost for the mitigation of the risk, in order to minimize the expected cost that is to arise in case the risk becomes a reality (Fig. 5).

3.1.3. Decision and implementation of risk management actions

اگه میشه لطف کنین و این مقالات رو از sience direct برام تهیه کنین

با تشکر

http://www.sciencedirect.com/science/article/pii/S1478409210000282
http://www.sciencedirect.com/science/article/pii/S0167923611002235
http://www.sciencedirect.com/science/article/pii/S0272696311000908

http://www.sciencedirect.com/science/article/pii/S1478409211000495

and new selection schemes
Konrad Diwold · Andrej Aderhold ·
Alexander Scheidler · Martin Middendorf
Received: 22 November 2010 / Accepted: 7 July 2011 / Published online: 24 July 2011
© Springer-Verlag 2011
Abstract The artificial bee colony optimization (ABC) is
a population-based algorithm for function optimization that
is inspired by the foraging behavior of bees. The population
consists of two types of artificial bees: employed bees (EBs)
which scout for new, good solutions and onlooker bees (OBs)
that search in the neighborhood of solutions found by the
EBs. In this paper we study in detail the influence of ABC’s
parameters on its optimization behavior. It is also investigated
whether the use of OBs is always advantageous. Moreover,
we propose two new variants of ABC which use new methods
for the position update of the artificial bees. Extensive
empirical tests were performed to compare the new variants
with the standard ABC and several other metaheuristics on a
set of benchmark functions. Our findings show that the ideal
parameter values depend on the hardness of the optimization
goal and that the standard values suggested in the literature
should be applied with care. Moreover, it is shown that in
some situations it is advantageous to use OBs but in others it
is not. In addition, a potential problem of the ABC is identified,
namely that it performs worse on many functions when
the optimum is not located at the center of the search space.
K. Diwold (B) · M. Middendorf
Department of Computer Science,
University of Leipzig, Leipzig, Germany
e-mail: kdiwold@informatik.uni-leipzig.de
M. Middendorf
e-mail: middendorf@informatik.uni-leipzig.de
A. Aderhold
School of Biology, University of St. Andrews,
St. Andrews, Fife, UK
e-mail: aa796@st-andrews.ac.uk
A. Scheidler
IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium
e-mail: ascheidler@iridia.ulb.ac.be
Finally it is shown that the new ABC variants improve the
algorithm’s performance and achieve very good performance
in comparison to other metaheuristics under standard as well
as hard optimization goals.
Keywords Swarm intelligence · Artificial bee colony
optimization · Function optimization
1 Introduction
Bio-inspired computation, i.e., the application of biological
principles in the context of computation, is as old as computer
science itself. Based on biological principles, several
prominent computational frameworks such as evolutionary
computation and neural networks have been developed (for
an extensive review of bio-inspired computation the interested
reader should refer to [13]).
A prominent subfield of bio-inspired computation is
swarm intelligence [8]. It applies concepts found in the collective
behavior of swarms such as fish shoals, bird flocks or
social insects to problems in various domains such as robotics
or optimization [7]. In optimization, swarm intelligence is
probably best known for ant colony optimization [11], which
utilizes the concept underlying the pheromone laying behavior
of ants; and particle swarm optimization (PSO), which
uses group flight guidance for optimization purpose [27].
In recent years bee-inspired algorithms have emerged
in the field of swarm intelligence. These algorithms are
based on mechanisms underlying the behavior of honeybees
and have been successfully applied to various problem
domains such as optimization [4], robotics [38], network
routing [44], multi-agent systems [31], and protein structure
prediction [3].
123
150 Memetic Comp. (2011) 3:149–162
Bee-inspired algorithms do not have a unified foundation,
but are based on different behavioral concepts
(see [21] or [10] for an in-depth review on bee-inspired optimization
approaches). In general one can distinguish between
two classes of algorithms: algorithms based on the mating
behavior of honeybees, and algorithms based on their foraging
behavior. Furthermore, a recent study [9] suggests that the
nest-site selection behavior of honeybees involves principles
that are interesting from an optimization point of view.
Mating-inspired algorithms draw their inspiration from
the genetic diversity underlying a bee colony. Genetic diversity
has been shown to be a driving factor in the ecological
success of bees [33] and is due to the polyandrous behavior
of a young queen on her maiden flight.Mating inspired algorithms
are closely related to evolutionary computation and
either introduce new bee-inspired mutation/crossover operators
in that context (e.g., [26,37]) or evolve populations by
imitating a queen’s maiden flight (e.g., [1,32]).
The second kind of algorithms are inspired by foraging
behavior. Foraging behavior of honeybees constitutes a
decentralized process that works on the basis of decisions of
individual bees. It enables a colony tomaintain a good ratio of
exploitation and exploration of food sources. In addition, foraging
is adaptive, meaning that a colony’s foraging effort can
adapt toward changing needs for resources if necessary [6].
Scouts that successfully locate a resource will return to the
hive and promote that resource by means of awaggle dance in
order to recruit other bees to forage on that resource [39]. As
well as the site’s distance and direction, the bee’s dance can
also encode its quality. Using this mechanism foragers can
distribute themselves over the available resources in terms of
profitability. A recent study [12] has shown that the recruitment
strategies used by honeybees are especially beneficial if
resources are of poor quality, few in number, and of variable
quality. A number of optimization algorithms have been proposed
on the basis of foraging, such as the bees’ algorithm
(BA) [35], the bee colony optimization algorithm (BCO)
[42] and the artificial bee colony optimization algorithm
(ABC) [17].
In this paper (which is an extended version of our
paper “Artificial Bee Colony Optimization: A New Selection
Scheme and Its Performance” at NICSO 2010) the artificial
bee colony optimization algorithm (ABC) is studied.
ABC was introduced by Karaboga in 2005 [17] and constitutes
one of the most prominent approaches in the field of
bee-inspired algorithms. The algorithm has been applied to
various problem domains including the training of artificial
neural networks [19,25], the design of a digital filters [18],
solving constrained optimization problems [22], and the prediction
of the tertiary structures of proteins [3]. Its optimization
performance has been tested and compared to other optimization
methods such as Genetic Algorithms (GA), PSO,
Particle Swarm Inspired Evolutionary Algorithm (PS–EA),
Differential Evolution (DE), and different evolutionary strategies
[23,24,20,2].
The ABC algorithm works with a population of artificial
bees. The bees are divided into two groups—employed bees
(EBs) are responsible for finding and maintaining promising
solutions, and onlooker bees (OBs) for performing local
search at these solutions. The exploration (via the EBs) and
exploitation (via the OBs) is influenced by the qualities of
the solutions currently maintained by the EBs. If an EB’s
solution does not improve over a certain number of steps it
will abandon it’s current solution and choose a new random
solution in the search space (EBs choosing a new random
solution are referred to as scouts).
ABC is clearly one of the most applied bee inspired algorithms
but several interesting properties have not been studied
so far. Therefore, one aim of this paper is to fill this gap
and to examine in detail the influence of several ABC key
parameters (i.e., size of the bee population, the ratio between
the number of employed bees and onlooker bees, and the
solution-abandon limit) on the optimization behavior. Some
of these parameters have been considered before [2,17,24]
and suggestions have been made regarding their parameterization.
However, our study shows that is necessary to reconsider
these settings because good parameter values depend
strongly on the optimization context (e.g., hardness of goal).
Moreover, the ratio between onlooker and employed bees is
studied, which has not been investigated in detail before.We
also test the ABC’s optimization performance in scenarios
where the global optimum is not located in the center of the
search space (which is the typical for applications).
The second aim of this paper is to propose two variants
of the standard ABC algorithm that use new methods for the
selection of new positions. To show the quality of the new
variants of ABC their performance is tested against the standard
ABC and several other population-based optimization
heuristics on several benchmark functions.
This paper is structured as follows. In Sect. 2 the ABC is
described. The newvariants ofABCare introduced in Sect. 3.
The experimental setup is described in Sect. 4 and the experimental
results are presented in Sect. 5. Concluding remarks
and an outlook are given in Sect. 6.
2 Artificial bee colony optimization
The ABC algorithm [17] is a population based algorithm for
function optimization that can be seen as aminimal honeybee
foraging model. The artificial bee population consists of two
types of bees: employed bees (EBs) and onlooker bees (OBs).
In ABC the search space represents the environment and
each point in the search space corresponds to a food source
(solution) that the artificial bees can exploit. The quality of
a food source is given by the value of the function to be

Volume 38, Issue 8, August 2012, Pages 1391–1403

http://www.sciencedirect.com/science/article/pii/S0301562912001561

لینک : http://onlinelibrary.wiley.com/doi/10.1002/nag.1610020303/abstract

لطفا مقاله بالا را براي من دانلود كنيد خييلي ممنون ميشم 

بررسي اثرات دما، تابش و تشعشع بر مشخصه هاي الكتريكي سلول خورشيدي و دريافت حداكثر توان از يك پانل خورشيدي با استفاده از سيستم ردياب نقطه ماكزيمم توان

http://www.freeuploadshare.com/files/rdfc41339324700.html

البته اگه دقیق تر بخوام بگم فعلا دنبال مقاله ای درباره تجهیزات و امکانات بازیافت و فرآوری سرباره های فولادی می گردم . منتظر راهنمایی شما دوستان هستم و مجددا از همه تشکر می کنم . موفق باشید

Title : Choosing the correct cooling-water program
Auteur : MACDONALD J. R.
Source : Chemical Engineering, Jan. 1987, vol. 94, no1, pp. 135-137

link: http://cat.inist.fr/?aModele=afficheN&cpsidt=8054844

http://rd.springer.com/article/10.1007/s11071-012-0464-5

http://pii.sagepub.com/content/early/2012/05/24/0959651812443922.abstract

http://www.filefactory.com/file/yxswz98zssl/n/fulltext_pdf

دوست و هموطن عزیزم اگر امکانش براتون هست این مقاله را برای من بفرستید بسیار متشکر میشوم. اتمام پایان نامه من بستگی به این مقاله دارد بسیار متشکرم

ممنون میشم اگه مقاله های زیر رو برای من فراهم کنید

A mathematical model for atmospheric ice accretion and water flow on a cold surface
T.G. Myers
J.P.F. Charpin

http://www.sciencedirect.com/science/article/pii/S0257897299000249
http://www.sciencedirect.com/science/article/pii/S0043164896074194
http://www.sciencedirect.com/science/article/pii/S0257897299002042
http://www.sciencedirect.com/science/article/pii/S0169433203008894
http://www.sciencedirect.com/science/article/pii/0257897295024727

اسم مقاله Web services security evaluation considerations

لینک مقاله http://dl.acm.org/citation.cfm?id=1577325

لطفا دانلودش کنید :)

مرسی

و تشکر از این کار نیکو

من به دنبال یک مقاله در رشته کامپیوتر می باشم به نام:

Grid resource discovery based on semantically linked virtual organizations

با تشکرات فراوان

عنوان مقاله:bott 1959, the mechanics of oblique slip faulting

من به مقاله ای که لینکش در زیر هست نیاز خیلی فوری و حیاتی دارم ممنون می شم کمکم کنید

http://dl.acm.org/citation.cfm?id=2037970.2038025&coll=DL&dl=GUIDE&CFID=99546767&CFTOKEN=14449659

Author:

Laurent Marteau

Published in:

· Journal

Network Security archive

Volume 2005 Issue 5, May, 2005
Elsevier Science Publishers B. V. Amsterdam, The Netherlands, The Netherlands
table of contentsdoi>10.1016/S1353-4858(05)70236-1
 

http://dl.acm.org/citation.cfm?id=2037970.2038025&coll=DL&dl=GUIDE&CFID=99546767&CFTOKEN=14449659

Author:

Laurent Marteau

Published in:
 
· Journal

Network Security archive

Volume 2005 Issue 5, May, 2005
Elsevier Science Publishers B. V. Amsterdam, The Netherlands, The Netherlands
table of contentsdoi>10.1016/S1353-4858(05)70236-1

 2005 Article

  Bibliometrics

· Downloads (6 Weeks): n/a
· Downloads (12 Months): n/a
· Citation Count: 0

This paper appears in:
Signal Processing, 2006 8th International Conference on
Date of Conference: 16-20 2006
Author(s): Ping Zhou
Biomed. Eng. Coll., Capital Univ. of Medical Sci., Beijing
Miao Yang ;  Ning Li
Volume: 4, Product Type: Conference Publications

please help me

Introduction to Stochastic Approximation
نویسنده:
George Yin
ژورنال مقاله:
Wiley Encyclopedia of Operations Research and Management Science
سال انتشار:
 2010
لینک دانلود مقاله:
http://onlinelibrary.wiley.com/doi/10.1002/9780470400531.eorms0428/pdf

http://www.sciencedirect.com/science/article/pii/001191648690007X

http://www.sciencedirect.com/science/article/pii/0196890494000533

http://www.sciencedirect.com/science/article/pii/019689049190100W

http://www.sciencedirect.com/science/article/pii/S0011916402009384

لطفا این مقاله را دانلود کنید

http://www.sciencedirect.com/science/article/pii/016920709090100P

دانلود مقاله شما : DOWNLOAD

http://www.sciencedirect.com/science/article/pii/S0011916406010770

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=285164&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D285164

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=330453&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D330453 

مقاله در خواستی شما

Journal of Corporate Finance - Capital structure and the control of managerial incentives

http://184.164.142.214/paper/sciencedirect/loan/112/HNZJtLMZYbuciScmLBzJd2Ak9POpKz/%5Bwww.sayens.org%5D_S0929119901000487.pdf

حجم فایل مقاله : 442.88 KB

========

با تشکر از زحمات شما اما لینک فوق موجود نمی باشد

مقاله در خواستی شما

Journal of Corporate Finance - Capital structure and the control of managerial incentives

http://184.164.142.214/paper/sciencedirect/loan/112/HNZJtLMZYbuciScmLBzJd2Ak9POpKz/%5Bwww.sayens.org%5D_S0929119901000487.pdf

حجم فایل مقاله : 442.88 KB

http://www.sciencedirect.com/science/article/pii/S0929119901000487

http://www.sciencedirect.com/science/article/pii/016920709090100P

Expert Systems with Applications - An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange

184.164.142.214/paper/sciencedirect/loan/108/6dT19MXpInPh2RbebciaYkhTPhspQK/%5Bwww.sayens.org%5D_S0957417410003453.pdf

Procedia - Social and Behavioral Sciences - The Contrastive Study of Mental Health Status of the Mothers of Exceptional Children (Deaf and Blind) In Special Educational System with Combined Educational System

184.164.142.214/paper/sciencedirect/loan/108/8k3vFaeqBpTSa9ImCML0NA9Sl12Am2/%5Bwww.sayens.org%5D_S1877042811021756.pdf

مطفا این مقاله رو برام بزارین

http://www.sciencedirect.com/science/article/pii/0029558258903456

من به این مقاله ها احتیاج دارم .ممنون میشم برام دانلود کنید

Fast and flexible genetic algorithm processor

On page(s): 635
Conference Location : Beirut
E-ISBN : 978-1-4577-1844-1
Print ISBN: 978-1-4577-1845-8
INSPEC Accession Number: 12488355
Digital Object Identifier : 10.1109/ICECS.2011.6122355
Date of Current Version : Mon Jan 16 00:00:00 EST 2012
Issue Date : 11-14 Dec. 2011

--------------------------------------------------------

Using a Genetic Algorithm and the Grid to Improve Transport Levels in the TJ-II Stellarator

On page(s): 81
Conference Location : Krakow
Print ISBN: 978-0-7695-3472-5
INSPEC Accession Number: 10438376
Digital Object Identifier : 10.1109/ISPDC.2008.30
Date of Current Version : Mon Dec 22 00:00:00 EST 2008
Issue Date : 1-5 July 2008

------------------------------------------------------

Discussion of evaluation methods for multiobjective interactive genetic algoritm

On page(s): 1
Conference Location : Kobe
ISSN : 2154-4824
E-ISBN : 978-1-889335-42-1
Print ISBN: 978-1-4244-9673-0
INSPEC Accession Number: 11699893
Date of Current Version : Fri Dec 10 00:00:00 EST 2010
Issue Date : 19-23 Sept. 2010

----------------------------------------------------

A hybrid intelligent Body Sensor Networks model
On page(s): 67
Conference Location : Melacca
Print ISBN: 978-1-4577-2151-9
INSPEC Accession Number: 12488078
Digital Object Identifier : 10.1109/HIS.2011.6122082
Date of Current Version : Thu Jan 05 00:00:00 EST 2012
Issue Date : 5-8 Dec. 2011

---------------------------------------------

Application Research of Data Mining Techniques in P2P Network

On page(s): 297
Conference Location : Guilin
Print ISBN: 978-0-7695-3899-0
INSPEC Accession Number: 11106299
Digital Object Identifier : 10.1109/WGEC.2009.208
Date of Current Version : Tue Feb 02 00:00:00 EST 2010
Issue Date : 14-17 Oct. 2009

-------------------------------------------

Fast and flexible genetic algorithm processor

On page(s): 635
Conference Location : Beirut
E-ISBN : 978-1-4577-1844-1
Print ISBN: 978-1-4577-1845-8
INSPEC Accession Number: 12488355
Digital Object Identifier : 10.1109/ICECS.2011.6122355
Date of Current Version : Mon Jan 16 00:00:00 EST 2012
Issue Date : 11-14 Dec. 2011
------------------------------------------------------

Genetic algorithms for self-spreading autonomous and holonomic unmanned vehicles in a three-dimensional space
On page(s): 1073
Conference Location : Baltimore, MD
ISSN : 2155-7578
Print ISBN: 978-1-4673-0079-7
INSPEC Accession Number: 12474895
Digital Object Identifier : 10.1109/MILCOM.2011.6127439
Date of Current Version : Thu Jan 12 00:00:00 EST 2012
Issue Date : 7-10 Nov. 2011

-----------------------------------------

Genetic algorithm and the problem of getting knowledge in e-learning systems
On page(s): 336
Print ISBN: 0-7695-2181-9
INSPEC Accession Number: 8476945
Digital Object Identifier : 10.1109/ICALT.2004.1357431
Date of Current Version : Mon Nov 22 00:00:00 EST 2004
Issue Date : 30 Aug.-1 Sept. 2004

An assessment of the quality of developmental day center playgrounds for exceptional children 

 آدرس مقاله: http://www.sciencedirect.com/science/article/pii/0190740979900227

The Contrastive Study of Mental Health Status of the Mothers of Exceptional Children (Deaf and Blind) In Special Educational System with Combined 

Educational System
 

آدرسش: http://www.sciencedirect.com/science/article/pii/S1877042811021756

بفرمایید

http://up98.org/upload/server1/02/h/ypmwq7herf3zz6csf3iz.pdf

امیوارم دیر نشده باشه

ممنون میشم

Fingerprint image processing using neural networks
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=152677&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D152677

=============================================================================================================================================
A two-step neural-network based algorithm for fast image super-resolution

http://www.sciencedirect.com/science/article/pii/S0262885606003684
=============================================================================================================================================
A neural network adaptive filter for the removal of impulse noise in digital images

http://www.sciencedirect.com/science/article/pii/089360809500128X
=============================================================================================================================================
 

کسی میتونه این مقاله را برایم ارسال کند؟

http://www.springerlink.com/content/4nq166282j3g5842

متشکرم

لینک دانلودشو واست گذاشتم

http://uplod.ir/l5qk66tc561l/1.pdf.htm

http://uplod.ir/9pbhuptk660e/2.pdf.htm

برای hosein

http://uplod.ir/v5pgyo63xq42/3.pdf.htm

یک مقاله از سایت scopus می خواستم

آدرسش اینه:

http://www.scopus.com/record/display.url?eid=2-s2.0-0025250805&origin=resultslist&sort=plf-f&src=s&st1=cambou&nlo=&nlr=&nls=&sid=wwBEdOWPJNN5Sr7d4yOKUnn%3a40&sot=b&sdt=cl&cluster=scoauthid%2c%227003986387%22%2ct&sl=19&s=AUTHOR-NAME%28cambou%29&relpos=30&relpos=10&searchTerm=AUTHOR-NAME%28cambou%29%20AND%20%28%20LIMIT-TO%28AU-ID,\%22Cambou,%20Bernard\%22%207003986387%29%20%29

عنوانش:

Determination of constitutive parameters from pressuremeter tests

Cambou, B.,Boubanga, A.,Bozetto, P.,Haghgou, M.

http://www.springerlink.com/content/37482g284061v451/

عنوان:

Effect of strain-softening on settlement due to tunnelling in soft soil

نویسندگان:

Ardie Purwodihardjo and Bernard Cambou

http://www.sciencedirect.com/science/article/pii/0305054880900143

نویسنده : J.N. Morse

ممنون می شم 

abdi.sa.ai@gmail.com

با تشکر فراوان

Pipe Drafting and Design
(Third Edition)
Author(s):
Roy A. Parisher

ISBN: 978-0-12-384700-3

لینک:

http://www.sciencedirect.com/science/book/9780123847003