A systematic review of quantitative risk analysis in construction of megaprojects

Mega projects (MP) require efficient management of risks during their construction. Therefore, it is crucial to identify any possible deviations towards meeting their objectives. Such deviation forced MP to be delivered behind schedule and over budget. According references, MP does not require only qualitative analysis but requires an accurate quantitative analysis based on knowledge and practice. Thus, this paper aims to undergo a systematic review of quantitative analysis literature in construction of worldwide MP,with the utmost aim to improve contractors quantitative risk analysis practices in the presence of uncertainty. A time line was produced which shows the process of quantitative risk analysis in this literature including the past six years from 2013 to 2018. This was followed by a critical analysis in order to account for quantitative risk analysis techniques highlighted throughout literature sources. Furthermore, the paper reviews the literature of worldwide mega projects by which quantitative risk analysis process was practiced. It was observed that Monte Carlo analysis technique has succeeded in supporting project managers in allocating deviations in the objectives of MP. The paper adds value to practitioners using the process of quantitative risk analysis as well as contractors working on construction of MP

Integrating the Principles of Lean Management into the Design Process of Green Tall Buildings

Tall buildings have become a sign of Economic and technological development in advanced countries, resolving different issues coming from the significant increase in the urban dwellers and land price. This type of buildings is characterized by its high levels of energy consumption and design complexity. The design process of tall buildings involves many professionals from several disciplines, requiring an efficient management to coordinate and interpret the interrelations between these various disciplines in the design stage taking into consideration the global awareness of energy consumption and the urgent need to reduce the quantity of energy used by this type of buildings. Lean process tools and methods have been adapted to the design management process in the AEC industry - known as Lean Design management (LDM) - to improve the context of design and overcome its deficiencies with minimum wastage with a greener comprehensive view of the whole project. Applying lean design management tools to the design process of tall buildings will help in overcoming the challenges coming from the complex design process of tall buildings. Thus, the objective of this paper is to set preliminary guidelines for the integration of the lean design management concept with the design process management of green tall buildings, through a literature review. More than 200 research paper -ranging from 2010 till present mostly indexed in Scopus database- including the aim of the study were selected for review. Research found a number of preliminary guidelines for optimizing the design process of green tall buildings through the adaptation of the lean design management concept. This research is meant to be the first phase of the study to assess the application of lean principles to the design process of tall buildings through practical analysis of case studies that will be carried out in further studies

Integrating the Principles of Lean Management into the Design Process of Green Tall Buildings

Tall buildings have become a sign of Economic and technological development in advanced countries, resolving different issues coming from the significant increase in the urban dwellers and land price. This type of buildings is characterized by its high levels of energy consumption and design complexity. The design process of tall buildings involves many professionals from several disciplines, requiring an efficient management to coordinate and interpret the interrelations between these various disciplines in the design stage taking into consideration the global awareness of energy consumption and the urgent need to reduce the quantity of energy used by this type of buildings. Lean process tools and methods have been adapted to the design management process in the AEC industry - known as Lean Design management (LDM) - to improve the context of design and overcome its deficiencies with minimum wastage with a greener comprehensive view of the whole project. Applying lean design management tools to the design process of tall buildings will help in overcoming the challenges coming from the complex design process of tall buildings. Thus, the objective of this paper is to set preliminary guidelines for the integration of the lean design management concept with the design process management of green tall buildings, through a literature review. More than 200 research paper -ranging from 2010 till present mostly indexed in Scopus database- including the aim of the study were selected for review. Research found a number of preliminary guidelines for optimizing the design process of green tall buildings through the adaptation of the lean design management concept. This research is meant to be the first phase of the study to assess the application of lean principles to the design process of tall buildings through practical analysis of case studies that will be carried out in further studies

Partial breaking of global D=4 supersymmetry, constrained superfields, and 3-brane actions

We show that the connection between partial breaking of supersymmetry and nonlinear actions is not accidental and has to do with constraints that lead directly to nonlinear actions of the Born-Infeld type. We develop a constrained superfield approach that gives a universal way of deriving and using these actions. In particular, we find the manifestly supersymmetric form of the action of the 3-brane in 6-dimensional space in terms of N=1 superfields by using the tensor multiplet as a tool. We explain the relation between the Born-Infeld action and the model of partial N=2 supersymmetry breaking by a dual D-term. We represent the Born-Infeld action in a novel form quadratic in the gauge field strengths by introducing two auxiliary complex scalar fields; this makes duality covariance and the connection with the N=1 supersymmetric extension of the action very transparent. We also suggest a general procedure for deriving manifestly duality symmetric actions, explaining in a systematic way relations between previously discussed Lorentz covariant and noncovariant actions.Comment: 28 pages, harvmac. v4: a mistake in eq.(3.24) (pointed out by S. Kuzenko) correcte

A Limited Sero-Surveillance of Foot and Mouth Disease in Sheep in Egypt

The present work was carried out to evaluate the current status of Foot and Mouth Disease (FMD) in sheep in Egypt. Blood-sera of sheep (N.= 100) located in different areas of 5 Governorates; Menofia, Giza, Fayoum, Beni-Sueif and Menia, were collected and serologically tested during 2010. Fifty samples of saliva and oesopharyngeal fluid were collected and examined virologically. Serum neutralization test and enzyme linked immunosorbant assay (ELISA) indicated that 16 % of the tested sera were positive to FMD-antibodies against type O1 and A. PrioCHECK blocking ELISA was achieved and indicated that 100% of detected FMD antibodies were due to infection. Trials of virus isolation in baby mice and calf kidney cell culture indicated that both types of FMD virus (O1 & A) are existed and persisted in Egyptian sheep. It could be recommended that great importance should be practiced of sheep vaccination with the local bivalent FMD vaccine to restrict the spread of infection and to control FMD

Innovative pH-responsive alginate-coated rosuvastatin-loaded chitosan nanoparticles: a targeted approach to inhibiting HMGB1-activated RAGE/TLR4-NFκB signaling in colonic inflammation in rats

This study developed and optimized an innovative oral pH-dependent drug delivery system utilizing rosuvastatin-loaded chitosan nanoparticles (RSV-CSNPs) coated with sodium alginate (ALG). The goal was to protect RSV-CSNPs from degradation in the acidic gastric environment and facilitate targeted sustained release in the colon to address inflammatory bowel disease. Nanoparticles were initially prepared by ionic gelation. A subsequent ALG coating process was optimized using a 23 factorial design. The optimal ALG-coated formulation demonstrated minimal drug loss (0.88% ± 0.09%), desirable particle size (407.2 ± 1.95 nm), and suitable zeta potential (−27.13 ± 1.36 mV). In vitro release tests highlighted the superiority of ALG-coated RSV-CSNPs, with significantly reduced RSV release in simulated gastric fluid (6.8% ± 1.06% after 2 h) compared to uncoated nanoparticles (38.45% ± 1.79%), affirming the protective effectiveness of the ALG coating. Extended-release studies at colonic pH (6.8) demonstrated sustained RSV release suitable for colon-specific targeting. In vivo assessments in a dextran sodium sulfate (DSS)-induced rat model of colitis revealed that ALG-RSV-CSNPs significantly outperformed both plain RSV and RSV-CSNPs. Treatment notably decreased colonic inflammation, disease severity scores, macroscopic damage, and oxidative stress indicators. Additionally, histopathological analyses showed remarkable restoration of colon tissue integrity, crypt preservation, and mucosal protection in animals treated with ALG-RSV-CSNPs. Mechanistically, ALG-RSV-CSNPs effectively attenuated colitis by significantly inhibiting the HMGB1-triggered RAGE/TLR4-NFκB inflammatory signaling pathway. Treatment resulted in substantial reductions in key inflammatory markers, including HMGB1, RAGE, TLR4 expression, NFκB activity, pro-inflammatory cytokines (TNF-α and IL-6), and apoptosis marker caspase-3. The anti-inflammatory actions were further supported by reduced neutrophil infiltration, lipid peroxidation, and enhanced antioxidant enzyme activities (SOD and GSH levels). The study identified HMGB1, RAGE, TLR4, and NFκB as critical biomarkers predicting disease activity. Correlation analysis highlighted strong positive associations among these markers, underscoring their collective involvement in colitis pathogenesis and emphasizing the multitarget therapeutic efficacy of ALG-RSV-CSNPs. Overall, this study demonstrates that the optimized pH-responsive ALG-coated RSV-CSNPs significantly enhance the therapeutic outcomes of RSV in colonic inflammation through targeted delivery and sustained release. These nanoparticles represent a promising strategy for effectively managing ulcerative colitis and related inflammatory bowel diseases. Future clinical studies are warranted to validate these findings and facilitate translation into human therapeutic applications

Early prediction keys for COVID-19 cases progression: A meta-analysis.

Backgroundː Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), within few months of being declared as a global pandemic by WHO, the number of confirmed cases has been over 75 million and over 1.6 million deaths since the start of the Pandemic and still counting, there is no consensus on factors that predict COVID-19 case progression despite the diversity of studies that reported sporadic laboratory predictive values predicting severe progression. We review different biomarkers to systematically analyzed these values to evaluate whether are they are correlated with the severity of COVID-19 disease and so their ability to be a predictor for progression. Methods: The current meta-analysis was carried out to identify relevant articles using eight different databases regarding the values of biomarkers and risk factors of significance that predict progression of mild or moderate cases into severe and critical cases. We defined the eligibility criteria using a PICO model. Results: Twenty-two relevant articles were selected for meta-analysis the following biomarkers C-reactive protein, interleukin-6, LDH, neutrophil, %PD-1 expression, D-dimer, creatinine, AST and Cortisol all recorded high cut-off values linked to severe and critical cases while low lymphocyte count, and low Albumin level were recorded. Also, we meta- analyzed age and comorbidities as a risk factors of progression as hypertension, Diabetes and chronic obstructive lung diseases which significantly correlated with cases progression (p < 0.05). Conclusionsː The current meta-analysis is the first step for analysing and getting cut-off references values of significance for prediction COVID-19 case progression. More studies are needed on patients infected with SARS-CoV-2 and on a larger scale to establish clearer threshold values that predict progression from mild to severe cases. In addition, more biomarkers testing also help in building a scoring system for the prediction and guiding for proper timely treatment

Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5