Risk Analysis of the Development of Sustainable Dongtan

Dongtan is set to be developed as a sustainable urban-rural integration,aiming to attract a wide range of commercial and leisure investments.The Shanghai Industrial Investment Corporation(SIIC),the largest international investment group owned by the Shanghai municipal government,is leading the Dongtan project in partnership with Arup.The project’s risks are categorized into eight major groups:(1)Force majeure,(2)people-related risks,(3)financial and economic risks,(4)political and country risks,(5)environmental risks,(6)completion-related risks,(7)design-related risks,and(8)technology risks.Among these,political risk is particularly notable for its high probability and significant impact.Effective project risk management is essential to foresee and address uncertainties that could jeopardize the project’s objectives and timelines.Appropriate strategies must be implemented to manage and mitigate these risks.

Cost Risk Appraisal： An Application of Project RiskManagement Process in Libyan Construction Projects

Projects delay and cost overrun have become general facts in the construction industry. Project cost risk analysis considers the different costs associated with a project and focuses on the uncertainties and risks that may affect these costs. An implementation of PRM （project risk management） process on regional construction project has been carried out to maximize the likelihood of project meeting its objectives within its constraints. Qualitative and quantitative risk analyses have been carried out. The qualitative analysis is presented in a table that shows top ranked risks in Libyan construction projects based on probability-impact grid technique. In quantitative risk analyses, Mont Carlo simulation technique has been conducted to quantify and evaluate the overall level of risk exposure associated with the project completion cost. A project simulation uses a model that translates cost uncertainties into their potential impact on project objectives. A frequency curve model that represents simulation results of project completion costs has been constructed. The frequency curve model shows all possible outcomes of expected project cost at different probabilities. Project manager or decision maker can select the appropriate project budget. If a probability of 0.95 confident project budget is selected that means cost overrun risk can be minimized to a probability of 0.05. It is very helpful for project manager to take decisions based on information that shows project completion cost and its associated probability rather than usin single information of estimated cost.

Risk Management and Operation Strategies of Chinese Overseas Risk Exploration Projects

Currently,the investment of oil and gas industry is still facing an unfavorable environment,in which,instable factors,such as financial crisis,terrorist,religious conflicts and rigorous environmental regulations,keep mucking up the business all around the world.Meanwhile,China’s rapid energy consumption growth boosted by a booming economy has put the country to rely heavily on exported oil.It is therefore extremely urgent to expand and diversify petroleum supply channel in consideration of the country’s energy security.As the world’s economy has been slowly recovering from the slump and

From risk management to uncertainty management: a significant change in project management

Starting with the meanings of the terms “risk” and “uncertainty,””he paper compares uncertainty management with risk management in project management. We bring some doubt to the use of “risk” and “uncertainty” interchangeably in project management and deem their scope, methods, responses, monitoring and controlling should be different too. Illustrations are given covering terminology, description, and treatment from different perspectives of uncertainty management and risk management. Furthermore, the paper retains that project risk management (PRM) processes might be modified to facilitate an uncertainty management perspective, and we support that project uncertainty management (PUM) can enlarge its contribution to improving project management performance, which will result in a significant change in emphasis compared with most risk management.

Managing project risks and uncertainties

This article considers threats to a project slipping on budget,schedule and fit-for-purpose.Threat is used here as the collective for risks（quantifiable bad things that can happen）and uncertainties（poorly or not quantifiable bad possible events）.Based on experience with projects in developing countries this review considers that（a）project slippage is due to uncertainties rather than risks,（b）while eventuation of some bad things is beyond control,managed execution and oversight are stil the primary means to keeping within budget,on time and fit-for-purpose,（c）improving project delivery is less about bigger and more complex and more about coordinated focus,effectiveness and developing thought-out heuristics,and（d）projects take longer and cost more partly because threat identification is inaccurate,the scope of identified threats is too narrow,and the threat assessment product is not integrated into overall project decision-making and execution.Almost by definition,what is poorly known is likely to cause problems.Yet it is not just the unquantifiability and intangibility of uncertainties causing project slippage,but that they are insufficiently taken into account in project planning and execution that cause budget and time overruns.Improving project performance requires purpose-driven and managed deployment of scarce seasoned professionals.This can be aided with independent oversight by deeply experienced panelists who contribute technical insights and can potentially show that diligence is seen to be done.

Risk Assessment and Prediction of Construction Project Based on 1D-CNN-Attention-BP

In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model project risk prediction model based on attention mechanism, one-dimensional convolutional neural network (1d-cnn) and BP neural network. Firstly, the literature analysis method is used to select the risk evaluation index value of construction project, and the attention mechanism is used to determine the weight of risk factors on construction period prediction;then, BP neural network is used to predict the project duration, and accuracy, cross entropy loss function and F1 score are selected to comprehensively evaluate the performance of 1d-cnn-attention-bp combined model. The experimental results show that the duration risk prediction accuracy of the risk prediction model proposed in this paper is more than 90%, which can meet the risk prediction of construction projects with high accuracy.

Investigation of Enterprise Informationization Project Risk

The enterprise informationization （El） project has already become modernization level and synthesis power of the enterprise. However, information project risk. In order to reduce the El project risk, it is necessary to adopt reasonable incentive and constraint effectively and to structure perfect market environment. an urgent work to raise the asymmetry increases the EI investment mode, to carry on incentive and constraint effectively and to structure perfect market environment.

Fuzzy Set-Based Risk Evaluation Model for Real Estate Projects

With the rapid development of residential real estate market, risk evaluation has been an important task in the process of project. This paper describes a risk evaluation method for residential real estate projects based on fuzzy set theory which uses linguistic variables and respective fuzzy numbers to evaluate the factors. The primary weights of factors and evaluation of alternatives are determined by applying linguistic variables and fuzzy numbers. The notion of Shapley value is used to determine the global value of each factor in accomplishing the overall objective of the risk evaluation process, so the primary weights are revised, thus the importance of factors can be reflected more precisely. A major advantage of the method is that it allows experts and engineers to express their opinions on project risk evaluation in linguistic variables rather than crisp values. An illustration is presented to demonstrate the application of the method in risk evaluation. The results are consistent with the results calculated by conventional risk evaluation method. The research demonstrates that the method is objective and accurate, and is of an application value in the risk evaluation for residential real estate project.

Research on the risk-evaluation model of power transmission and transformation projects based on multilevel fuzzy-thought weighting for cloud-model improvement

The risk evaluation of power transmission and transformation projects is a complex and comprehensive evaluation process influenced by many factors and involves many indicators.In order to solve the uncertainty and fuzziness problems in the process of the multilevel fuzzy risk evaluation of power transmission and transformation projects,this paper introduces the cloud theory,which is specialized in the study of uncertainty problems and constructs the multilevel fuzzy comprehensive risk-evaluation model of power transmission and transformation projects based on the improved multilevel fuzzy-thought weighting based on the cloud model.Finally,the risk of the Beijing 220-kV Tangyu power transmission and transformation project is evaluated and the feasibility of the evaluation model is verified.The results of the evaluation and the evaluation layer cloud model are combined with MATLAB simulation to show that the risk level of the project is between large risk and general risk.

Climate change and Aedes albopictus risks in China:current impact and future projection

Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue.

RISK ANALYSIS OF EROSION BELOW THE DAM OF ER TEN PROJECT

The erosion risk below the dam of Er Tan project, which comes from the flood relief of the spillway and mid- dle outlet spillway, is analysed by risk analysis theory. According to the analysis results, it is imperative that the stilling pool below the dam should be adopted to protect river bed from erosion. From the view of risk-protection and economy, the Er Tan project design scheme that adopted the stilling pool is coincident with safe and economical rules. It is efficient and scientific. The erosion risk analysis method used in the paper can be used in other projects. The results are certainly of reference value and great significance for engineering design.

Modelling the iron ore price index:A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource,predicting its price is strategically important for risk management at related enterprises and projects.Based on a signal decomposition technology and an artificial neural network,this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China's iron ore futures and spot markets,and to forecast the price index series of China's and international iron ore spot markets from the futures market.The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets.However,the forward price in China's iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market,and was therefore unable to fully disseminate domestic iron ore market information.The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets.The results are valuable ref-erences for the early-warning and management of the related enterprise project risks.

Measuring project performance by applying social network analyses

It is often argued that the core of organizational success is efficient collaboration.Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual skills or expertise.However,the lack of efficient models to manage collaboration properly is a major constraint for organizations to profit from internal and external collaborative initiatives.Currently,much of the collaboration in organizations occurs through virtual network channels,such as e-mail,Yammer,Jabber,Microsoft Teams,Skype,and Zoom.These are even more important in situations where different time zones and even threats of a pandemic constrain face-to-face human interactions.This work introduces a multidisciplinary heuristic model developed based on project risk management and social network analysis centrality metrics graph-theory to quantitatively measure dynamic organizational collaboration in the project environment.A case study illustrates the proposed model’s implementation and application in a real virtual project organizational context.The major benefit of applying this proposed model is that it enables organizations to quantitatively measure different collaborative,organizational,and dynamic behavioral patterns,which can later correlate with organizational outcomes.The model analyzes three collaborative project dimensions:network collaboration cohesion evolution,network collaboration degree evolution,and network team set variability evolution.This provides organizations an innovative approach to understand and manage possible collaborative project risks that may emerge as projects are delivered.Organizations can use the proposed model to identify projects’critical success factors by comparing successful and unsuccessful delivered projects’dynamic behaviors if a substantial number of both project types are analyzed.The proposed model also enables organizations to make decisions with more information regarding the support for changes in observed collaborative patterns as demonstrated by statistical models in general,and linear regressions in particular.Further,the proposed model provides organizations with a completely bias-free data-collection process that eliminates organizational downtime.Finally,applying the proposed model in organizations will reduce or eliminate the risks associated with virtual collaborative dynamics,leading to the optimized use of resources;this will transform organizations to become more lean-oriented and significantly contribute to economic,social,and environmental global sustainability.