Suchergebnisse

The world is rapidly advancing towards the electrification of mobility owing to the substantial benefits of emission reduction. Adhering to international trends and environmental obligations, the Government of Pakistan (GOP) also intended to adopt 30@30 plug-in-electric vehicles (PEVs) across the country, which implies 30 percent of new sales will be of PEVs until 2030. Despite the policy guidelines introduced by the GOP as well as incentives for vehicle fleet electrification and indigenization, the foremost challenge is the lack of a PEV charging infrastructure placement plan for the country. In this regard, an optimal locality map for level-3 or direct current fast charging (DCFC) stations' installation is proposed, considering traffic volume, service area, and local grid facility while ensuring the availability of charging stations across all major networks of the country. The area of focus for this is National Highway 5, known as N5, and the Motorway-2 (M2) Network. The paper also provides insights into the techno-economic analysis of the proposed charging station installation spots. The results are extremely encouraging and reveal the proposed PEV charging stations under observation on the highways from Lahore to Islamabad consumed an electricity share of 3 MW–0.13 MW based on minimum to maximum traffic volume scenarios, respectively. The study is impactful and ultimately paves a way forward for the aggravation of the EV market share by considering the initial investment and a payback period of 7 months. With the help of this study, better planning in terms of EV penetration size and its requirement for public DCFC stations can be implemented, and the exact recipe for the growth of the supportive industry with the pace of PEVs' perforation can be executed.

AbstractMost risk analysis approaches are static; failing to capture evolving conditions. Blowout, the most feared accident during a drilling operation, is a complex and dynamic event. The traditional risk analysis methods are useful in the early design stage of drilling operation while falling short during evolving operational decision making. A new dynamic risk analysis approach is presented to capture evolving situations through dynamic probability and consequence models. The dynamic consequence models, the focus of this study, are developed in terms of loss functions. These models are subsequently integrated with the probability to estimate operational risk, providing a real‐time risk analysis. The real‐time evolving situation is considered dependent on the changing bottom‐hole pressure as drilling progresses. The application of the methodology and models are demonstrated with a case study of an offshore drilling operation evolving to a blowout.

This book highlights the critical relationship between China and the Association of Southeast Asian Nations (ASEAN) associated with its trade interdependency. As the largest trade partner in the region, China has not only presented itself with opportunities for ASEAN to tap its market, but also created great challenges for the region. The fundamental question that this book addresses, therefore, is whether China's engagement with ASEAN comes at a cost for the latter following from the systemic risks tied to the China-centric supply chains in the region. The trade interactions between China and ASEAN, though extensively explored, are less understood in the context of its influence over the region amidst the recent changing dynamics that follow from China's global engagement and backlash from major powers. The book therefore resolutely rises against stereotypes and clichés, making readers reconsider many oversimplified assumptions of the benefits of trade engagement where economies are interconnected through complex production chains. .

AbstractData mining (DM) has been applied in many advanced science and technology fields, but it has still not been used for domino effect risk management to explore minimum risk scenarios. This work investigates the feasibility of DM in minimizing the risk of fire‐induced domino effects in chemical processing facilities. Based on DM, an evidential failure mode and effects analysis (E‐FMEA), which could bridge chemical facilities' operational reliability and domino effect risk, is combined with fault tree analysis (FTA) for the occurrence risk modeling of loss of containment (LOC) event of chemical facilities, which is often the triggering point of fire‐induced domino effects. Industry specific data such as reliability data, inspection records, and maintenance records are of great value to model the potential occurrence criticality of LOC. The data are used to characterize the LOC risk priority number (RPN) of chemical facilities through FTA and E‐FMEA, search and statistics rules are proposed to mine inspection records to assess LOC risk factors. According to the RPN scores of facilities, inherent safety strategies to minimize risk via inventory control are proposed, and their effectiveness is tested using a well‐known probit model. In this way, the approach proposes a unit‐specific evidence‐based risk minimization strategy for fire‐induced domino effects. A case study demonstrates the capability of DM in the risk minimization of fire‐induced domino effects.

The paper deals with the architecture and function of watermills in Swat valley. Watermill is a seldom-used term; however, it has played a significant role in the socio-cultural and economic lives of people in the past. This research work explores the case study of water mills in the Swat region. It examined in detail its processing and operation. The watermill was not only an instrument used for grinding purposes but also determined the mode of production, class system and social values of people. Modern technology has though changed people's behaviors and social formations up to a large extent, but it couldn't erase people's memories and history. A qualitative method has been used for conducting this research work. An ethnic-archaeological method was focused on recording the history of this tremendous ancient technology which contributed widely to the socio-cultural context of people.

The study inspects the size and liquidity pattern in Pakistan equity market. Sample size contains 278 non-financial firm's monthly data listed on Pakistan Stock Exchange (PSX) from 2001 to 2012. This study uses three asset pricing models (eq.5), (eq.6) and (eq.7). Four factors asset pricing model estimates that momentum factor is positively and negatively linked with winner and loser stocks, both in size and liquidity patterns. Although it is observed that the presence of size and liquidity does not affect the coefficient results but average value of momentum premium in larger in liquidity than size pattern. Further, the study reveals high average stock returns on momentum strategy in liquidity pattern than size that is 8.05% Vs 6.67%, respectively. Results of this study contradicts Fama and French (2012) who concluded that size pattern in momentum factor outperform the equity market. But this study conclude that liquidity pattern outperforms the size pattern in momentum factor. This study raises the question that should investors and academicians consider size or liquidity pattern in momentum factor for high returns and future research?

AbstractSafety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation‐based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source‐to‐source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well‐established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents' relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor‐based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.

A comprehensive methodology for economic consequence analysis with appropriate models for risk analysis of process systems is proposed. This methodology uses loss functions to relate process deviations in a given scenario to economic losses. It consists of four steps: definition of a scenario, identification of losses, quantification of losses, and integration of losses. In this methodology, the process deviations that contribute to a given accident scenario are identified and mapped to assess potential consequences. Losses are assessed with an appropriate loss function (revised Taguchi, modified inverted normal) for each type of loss. The total loss is quantified by integrating different loss functions. The proposed methodology has been examined on two industrial case studies. Implementation of this new economic consequence methodology in quantitative risk assessment will provide better understanding and quantification of risk. This will improve design, decision making, and risk management strategies.