Ultimate torsional strength of cracked stiffened box girders with a large deck opening

AbstractThe present paper studies the ultimate torsional strength of stiffened box girders with large deck opening due to the influence of cracks. Three types of hull girders with different spans are provided for comparison. Potential parameters which may have effects on the torsional strength including the mesh refinement, initial deflection, material strain hardening, geometric properties of crack and stiffener are discussed. Two new concepts that play an significant role in the ultimate strength research of damaged box girders are introduced, one of which is the effective residual section (ERS), the other is the initial damage of the failure zone (IDFZ) for intact structures. New simple formulas for predicting the residual ultimate torsional strength of cracked stiffened box girders are derived on the basis of the two new concepts

Analysis the Role Conflict of Trade Union Chairman and Its Types

With the development of the market economy, the trade union work has become increasingly complex. As a special group, trade union chairman plays multiple roles. On the one hand, the trade union chairman should safeguard the rights and interests of enterprise employees, on the other hand, as the workers of the enterprise, they have to create benefits for the enterprise, this kind of dual role trigger a trade union chairman role conflict. In this paper, through reviewing the relevant literature, from the personal angle of the trade union chairman, trade union chairman of role conflict from three dimensions divided responsibility, time, interests. Through the division of the three dimension classifying trade union chairman of role conflict, and explain the causes of different types of role conflict and the corresponding countermeasures and suggestions, so as to eliminate the role of the trade union chairman conflict, give full play to the role of the trade union chairman

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration. It is responsible for the unreliability of practical machine learning systems. For example, in online advertising, an ad can receive a click-through rate prediction of 0.1 over some population of users where its actual click rate is 0.15. In such cases, the probabilistic predictions have to be fixed before the system can be deployed. In this paper, we first introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We show that existing post-hoc calibration methods have limited improvements in the new field-level metric and other non-calibration metrics such as the AUC score. To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set. We present extensive experiments on five large-scale datasets. The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier score and AUC, as well as the proposed field-level calibration error.Comment: WWW 202

A collaborative trust management scheme for emergency communication using delay tolerant networks

Delay Tolerant Network (DTN) comprises of nodes with small and limited resources including power and memory capacity. We propose the use of DTN as an alternate means of communication for the dissemination of emergency information in a post-disaster evacuation operation. We investigate the performance of DTN in providing emergency communication support services under packet dropping attacks. We consider internally motivated attacks where the nodes that are part of the emergency rescue team are compromised with malicious behaviours thereby dropping packets to disrupt the message dissemination during the evacuation operation. A way to mitigating malicious behaviour and improve network performance of DTN is to use incentives in exchanging information between nodes. Unlike existing schemes, we consider the Basic Watchdog Detection System which detects and acts against misbehaving nodes to reduce their overall impact on the network performance. We design a Collaborative Trust Management Scheme (CTMS) which is based on the Bayesian detection watchdog approach to detect selfish and malicious behaviour in DTN nodes. We have evaluated our proposed CTMS through extensive simulations and compared our results with the other existing schemes. Our evaluations show that the use of adequate collaborative strategies between well behaved nodes could improve the performance of Watchdog schemes taking into account the delivery ratio, routing cost and the message delay from the source node to the destination node

Electric Vehicle Charging Recommendation and Enabling ICT Technologies: Recent Advances and Future Directions

The introduction of Electric Vehicles (EV) will have a significant impact on the sustainable economic development of urban city. However, compared with traditional gasoline-powered vehicles, EVs currently have limited range, which necessitates regular recharging. Considering the limited charging infrastructure currently available in most countries, infrastructure investments and Renewable Energy Sources (RES) are critical. Thus, service quality provisioning is necessary for realizing EV market. Unlike numerous previous works which investigate "charging scheduling" (referred to when/whether to charge) for EVs already been parked at home/Charging Stations (CSs), a few works focus on “charging recommendation” (refer to where/which CS to charge) for on-the-move EVs. The latter use case cannot be overlooked as it is the most important feature of EVs, especially for driving experience during journeys. On-the-move EVs will travel towards appropriate CSs for charging based on smart decision on where to charge, so as to experience a shorter waiting time for charging. The effort towards sustainable engagement of EVs has not attracted enough attention from both industrial and academia communities. Even if there have been many charging service providers available, the utilization of charging infrastructures is still in need of significant enhancement. Such a situation certainly requires the popularity of EVs towards the sustainable, green and economic market. Enabling the sustainability requires a joint contribution from each domain, e.g., how to guarantee accurate information involved in decision making, how to optimally guide EV drivers towards charging place with the least waiting time, how to schedule charging services for EVs being parked within grid capacity. Achieving this goal is of importance towards a positioning of efficient, scalable and smart ICT framework, makes it feasible to learn the whole picture of grid: - Necessary information needs to be disseminated between stakeholders CSs and EVs, e.g., expected queuing time at individual CSs. In this context, how accurate CSs condition information plays an important role on the optimality of charging recommendation. - Also, it is very time-consuming for the centralized Global Controller (GC) to achieve optimization, by seamlessly collecting data from all EVs and CSs, The complexity and computation load of this centralized solution, increases exponentially with the number of EVs. This paper summaries the recent interdisciplinary research works on EV charging recommendation along with novel ICT frameworks, with an original taxonomy on how Intelligent Transportation Systems (ITS) technologies support the EV charging use case. Future directions are also highlighted to promote the future research