Technical Challenges Facing Regulators–Assessment of Long-Term Materials Performance

1987 ◽  
Vol 112 ◽  
Author(s):  
Kenneth W. Stephens
Keyword(s):  
Real Time ◽  
Waste Disposal ◽  
Time Frame ◽  
High Level Waste ◽  
Short Term ◽  
Long Time ◽  
Computer Codes ◽  
High Level ◽  
Term Data

AbstractFor a number of years, nuclear regulators have grappled with difficult questions such as: “How safe is safe enough?” Such issues take on new dimensions in the long time-frame of high-level waste disposal.Many of the challenges facing regulators involve assessment of long-term materials performance. Because real-time experiments cannot be conducted, it is necessary to rely extensively on modeling. This raises issues regarding the extent to which long-term extrapolations of short-term data are justified, the question of how closely models must represent reality to be trusted, and practical matters such as methods for validating unique computer codes.Issues such as these illustrate how regulators must make decisions in a climate of uncertainty. Methods used by non-technical disciplines to make decisions under uncertainty have been examined and offer solutions for regulators and licensees alike.

Issues in the Timing of High Level Waste Disposal: An International Perspective

2008 ◽  
Author(s):  
Stan Gordelier ◽  
Pa´l Kova´cs
Keyword(s):  
Radioactive Waste ◽  
Waste Disposal ◽  
Nuclear Power ◽  
Nuclear Industry ◽  
High Level Waste ◽  
Geological Disposal ◽  
The Public ◽  
Deep Geological Disposal ◽  
High Level

The world is facing energy difficulties for the future, in terms of security of supply and climate change issues. Nuclear power is virtually carbon free and it contributes to energy security, being a quasi-domestic source. Whilst it cannot provide a complete answer to these challenges, it is certainly capable of providing a significant component of the answer. However, nuclear power remains controversial. In order to gain public acceptance, it is widely recognised that a number of key issues need to be addressed, amongst which is resolution of the high-level radioactive waste (HLW) (including spent fuel) disposal issue. This is an important issue for all countries with an existing nuclear programme, whether or not it is intended that nuclear power should be phased out or expanded — the waste already exists and must be managed in any event. It is equally important for countries planning a new nuclear power programme where none has previously existed. Since nuclear power was first developed over fifty years ago, HLW arisings have been stored as an interim measure. It is widely believed by experts (though not by many opponents of the nuclear industry, nor by the public) that deep geological disposal, after a reasonable cooling time in interim storage, is technically feasible and constitutes a safe option [1] at an acceptable cost. The total volume of HLW from nuclear reactors is relatively small. A key issue, however, is the time-scale for developing such a final disposal solution. Considerations of security and inter-generational equity suggest that geological disposal should be implemented as soon as possible irrespective of whether or not new arisings are created. The question of managing HLW is not necessarily related to the issue of building new nuclear power stations. However, many opponents argue that there has been insufficient demonstration of the long-term safety of deep geological disposal. The same opponents also argue that there should be a moratorium on building new nuclear power plants (NPPs) until the issue of long-term management of HLW is resolved. These arguments have a powerful influence on public opinion towards both the construction of a waste repository and the building of new NPPs. The intent of this paper (developed from the current OECD NEA study on “Timing of High Level Waste Disposal”) is to identify and discuss some of the factors influencing the timing of the implementation of a HLW disposal strategy and to demonstrate to decision makers how these factors are affecting country strategies, based on current experience. Determining an optimum timescale of HLW disposal may be affected by a wide range of factors. The study examines how social acceptability, technical soundness, environmental responsibility and economic feasibility impact on the timing of HLW disposal and can be balanced in a national radioactive waste management strategy taking the social, political and economic environment into account. There is clear evidence that significant fractions of the public still have serious misconceptions with respect to the issues surrounding nuclear waste. The nuclear industry, together with governments in those countries who would like a component of nuclear power in their energy mix, has a responsibility for and a significant challenge in presenting its case to the public.

Prediction of Long Term Stress Rupture Data for 2124

2006 ◽  
Vol 519-521 ◽  
pp. 1041-1046 ◽  
Author(s):  
Brian Wilshire ◽  
H. Burt ◽  
N.P. Lavery
Keyword(s):  
Q Methodology ◽  
Fracture Behavior ◽  
Stress Rupture ◽  
Creep Data ◽  
Short Term ◽  
Rupture Data ◽  
Term Creep ◽  
Term Data ◽  
Short Term Creep

The standard power law approaches widely used to describe creep and creep fracture behavior have not led to theories capable of predicting long-term data. Similarly, traditional parametric methods for property rationalization also have limited predictive capabilities. In contrast, quantifying the shapes of short-term creep curves using the q methodology introduces several physically-meaningful procedures for creep data rationalization and prediction, which allow straightforward estimation of the 100,000 hour stress rupture values for the aluminum alloy, 2124.

scholarly journals Smart carbon monitoring platform under IoT-Cloud architecture for small cities in B5G

2021 ◽  
Author(s):  
He Zhang ◽  
Jianxun Zhang ◽  
Rui Wang ◽  
Yazhe Huang ◽  
Mengxiao Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Carbon Emission ◽  
Emission Measurement ◽  
Low Carbon ◽  
Time Data ◽  
Small Cities ◽  
Real Time Data ◽  
Monitoring Platform ◽  
Term Data

AbstractWith the rapid development of the Internet of Things (IoT) in the 5G age, the construction of smart cities around the world consequents on the exploration of carbon reduction path based on IoT technology is an important direction for global low carbon city research. Carbon dioxide emissions in small cities are usually higher than that in large and medium cities. However, due to the huge difference in data environment between small cities and Medium-large sized cities, the weak hardware foundation of the IoT, and the high input cost, the construction of a small city smart carbon monitoring platform has not yet been carried out. This paper proposes a real-time estimate model of carbon emissions at the block and street scale and designs a smart carbon monitoring platform that combines traditional carbon control methods with IoT technology. It can exist long-term data by using real-time data acquired with the sensing device. Therefore, the dynamic monitoring and management of low-carbon development in small cities can be achieved. The contributions are summarized as follows: (1) Intelligent thermoelectric systems, industrial energy monitoring systems, and intelligent transportation systems are three core systems of the monitoring platform. Carbon emission measurement methods based on sample monitoring, long-term data, and real-time data have been established, they can solve the problem of the high cost of IoT equipment in small cities. (2) Combined with long-term data, the real-time correction technology, they can dispose of the matter of differences in carbon emission measurement under diverse scales.

From Users’ Intentions to IF-THEN Rules in the Internet of Things

2021 ◽  
Vol 39 (4) ◽  
pp. 1-33
Author(s):  
Fulvio Corno ◽  
Luigi De Russis ◽  
Alberto Monge Roffarello
Keyword(s):  
Internet Of Things ◽  
Smart Devices ◽  
The Internet ◽  
Short Term ◽  
Real World Data ◽  
Original Intention ◽  
Recommendation Accuracy ◽  
High Level ◽  
The Internet Of Things

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

scholarly journals A Framework for Property Developers to Survive in a Recession

10.29007/cfr2 ◽  
2018 ◽  
Author(s):  
Zunoon Parambath ◽  
Nilupa Udawatta
Keyword(s):  
Survival Strategies ◽  
Economic Activities ◽  
Short Term ◽  
Property Development ◽  
Market Shares ◽  
Negative Impacts ◽  
Set Up ◽  
High Level ◽  
Property Developers

Recession is considered as a major threat to the economy as it slows down economic activities. The property development sector is extremely responsive to these economic conditions. Thus, it is crucial to understand causes, effects and strategies for property developers to survive in a recession without any ill effects. Thus, this research aimed to develop a framework for property developers to identify appropriate survival strategies in recession. A comprehensive literature review was conducted in this research to achieve the above mentioned aim. The results of this study indicated that recession prompts negative impacts on property development sector resulting in unemployment, lower demand, production and revenue, decline in resources and high level of competition. According to the results, the survival strategies were classified into short-term and long-term strategies. The short term strategies include: implementing management tactics, cut down of operating costs, keeping financing lines set up, timely repayment of debts, setting vital new objectives for the future, undertaking shorter time span developments, specialisation in favoured market, renegotiating deals and contracts. The long-term strategies include retrenchment, restructuring, investment and ambidextrous strategies. Similarly, attention should be paid to predict any changes in the economic environment that can influence property development activities and it is necessary to carefully evaluate investment activities to increase sales, profits and market shares of property developers. Preparing for a crisis is doubtlessly the ideal approach as it can facilitate both survival and growth. Thus, the property developers can implement these suggested strategies in their businesses to enhance their practices.

scholarly journals Methods of long-term and short-term adaptation for biometric image of keystroke dynamics

2021 ◽  
Vol 7 (3D) ◽  
pp. 450-457
Author(s):  
Dmitry V. Pashchenko ◽  
Dmitry A. Trokoz ◽  
Alexey I. Martyshkin ◽  
Elena A. Balzannikova
Keyword(s):  
Working Capacity ◽  
Keystroke Dynamics ◽  
Short Term ◽  
User Identification ◽  
New Device ◽  
Short Term Adaptation ◽  
Long Time

This article discusses one of the main problems of user identification by keyboard handwriting - short-term changes in the keystroke dynamics of users in connection with its psychophysical state, as well as changes over a long time associated with the formation of keystroke dynamics by a new user or when switching to a new device. A method for determining the phase of working capacity by the time characteristics of the keystroke dynamics is proposed.

scholarly journals Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

2020 ◽  
Vol 34 (05) ◽  
pp. 9571-9578 ◽  
Author(s):  
Wei Zhang ◽  
Yue Ying ◽  
Pan Lu ◽  
Hongyuan Zha
Keyword(s):  
State Of The Art ◽  
Natural Extension ◽  
Target Image ◽  
Short Term ◽  
Image Representations ◽  
High Level ◽  
Image Descriptions ◽  
Shed Light ◽  
Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

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