Quantitative Assessment of Building Constructability Using BIM and 4D Simulation

Despite the fact that applying constructability concepts in building designs have led to savings estimated within a range of 1% to 14% of the capital cost, the construction industry still lacks an advanced tool to assess and check for constructability implementations in designs. Therefore, the objective of this research is to propose a quantitative assessment of building constructability, which transforms the subjective assessment of constructability knowledge to a quantified value so that it is easy to analyze and improve building design. The proposed methodology uses the advancement of object oriented Building Information Model (BIM) and 4D simulation model to serve as a data repository models for the constructability assessment platform. Factors affecting constructability of building designs have been identified and relatively weighted using Analytical Hierarchy Process (AHP) technique based on a questionnaire survey collected throughout the Canadian provinces. Evaluation criteria are also developed to assist the designer to evaluate the design proposals. The outcome shows that the proposed method provides the designer an accurate and faster mode in evaluating project constructability by using the parametric features from BIM and taking the advantage of spatial relationship between building components.

Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution.However,there is,currently,nearly no standard technical framework for objective and quantitative intelligence evaluation.In this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation theories.On this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results.Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

A methodological framework of landslide quantitative risk assessment in areas with incomplete historical landslide information

Landslide risk assessment(LRA)is of great significance to hazard prevention and mitigation.However,the historical landslide information is incomplete in most areas,which makes the landslide quantitative risk assessment(LQRA)extremely difficult.This research proposed a set of frameworks for LQRA,so as to achieve LQRA in areas with incomplete historical landslide information.Firstly,we constructed the convolutional neural network(CNN)model suitable for landslide susceptibility assessment(LSA)by studying the structure and hyperparameters optimization of CNN.Secondly,we proposed a method to calculate the temporal probability by using the Poisson model based on the time range of historical landslides occurrence,and then conducted landslide hazard assessment(LHA).Then,we established a mathematical model for landslide intensity of shallow landslide based on landslide area and slope,aiming at solving the problem that it is difficult to calculate landslide intensity due to the lack of landslide volume and velocity.Based on the landslide intensity and the hazard-resistant capacity of the element at risk,we assessed the landslide vulnerability.Finally,population risk map and economic risk map are obtained based on the landslide hazard,vulnerability,and estimated value of the elements at risk.The proposed LQRA framework was applied to Tumen City,China for testing and field validation.From the results,the CNN model built can help improve the accuracy of LSA.The proposed temporal probability calculation method is conducive to the completion of LHA in areas with incomplete historical landslide information.The established landslide intensity mathematical model has certain credibility.Since the landslide risk map is obtained through appropriate simplification and substitution estimation,its final value cannot be used as an accurate prediction of future losses,but it can be used as a reference for the extent of potential losses,so as to determine the areas where hazard prevention and mitigation measures need to be taken.

Quantitative assessment and visualization of flight risk induced by coupled multi-factor under icing conditions

Aircraft icing has been proven to be one of the most serious threats to flight safety. During the analysis of flight risk under icing conditions, quantitative assessment and visualization of flight risk are quite essential as they provide safe manipulation strategies in intricate conditions.However, they are rarely studied. Since the icing flight accidents are the result of the coupling of multiple unfavorable factors, in present study, we have proposed a method to quantitatively assess flight risk induced by multi-factor coupling under icing conditions by Monte-Carlo simulation and multivariate extreme value theory. The results demonstrate that the flight risk probability increases with the rise of unfavorable factors. Besides, a flight risk visualization method named flight safety window has been presented to build the flight risk distribution cloud maps in different complex conditions. The cloud maps show that the icing would give rise to atrophy of the safety scope, and the consequence would be even more severe when coupled with other more unfavorable factors. The proposed methods in this study would be useful in flight risk analysis under icing conditions and can enhance the pilot's situational awareness in selecting correct strategies within the safety zone to avoid unsafe manipulation.

Quantitative assessment of rhodamine spectra

With the development of single-molecule detection and super-resolution fluorescence imaging,rhodamine dyes gain new life.Through the modification of the N-substituents a nd the replacement of the oxygen atom in xanthene,the wavelength and brightness can be effectively changed.However,the spectra of rhodamine,especially due to the balance between ring-closed non-fluorescent lactone and ring-opened fluorescent zwitterion/cation,are sensitive to interference from various environmental facto rs,In this way,the spectral data of various rhodamines repo rted by different re search groups under different test conditions lacked comparability,sometimes even lacked accuracy.In order to meet the requirements for the accuracy and uniformity of spectral data in the research of single molecule imaging and dye structure-fluorescence relationship study,we have tested the spectra of fifteen rhodamine dyes that cover the visible and near-infrared regions under exactly the same conditions.By studying the dependence of the spectra on dye concentrations,it was confirmed that 1 μmol/L was ideal fo r detection less from the interference of dye molecule aggregation.We provide compre hensive and reliable spectral data of these fifteen dyes,which are expected to be used as references for future research.And the direct comparison of different rhodamine spectra would help to understand the structure-fluorescence relationship of rhodamines.

A Quantitative Method for Active Fault Migration Distance Assessment on both Sides of Mid-Ocean Ridges——Based on Multi-Beam Data

Fracture-fissure systems found at mid-ocean ridges are dominating conduits for the circulation of metallogenic fluid.Ascertaining the distribution area of active faults on both sides of mid-ocean ridges will provide a useful tool in the search for potential hydrothermal vents,thus guiding the exploration of modern seafloor sulfides.Considering the MidAtlantic Ridge 20°N–24°N(NMAR)and North Chile Rise(NCR)as examples,fault elements such as Fault Spacing(?S)and Fault Heave(?X)can be identified and quantitatively measured.The methods used include Fourier filtering of the multi-beam bathymetry data,in combination with measurements of the topographic slope,curvature,and slope aspect patterns.According to the Sequential Faulting Model of mid-ocean ridges,the maximal migration distance of an active fault on either side of mid-ocean ridges—that is,the distribution range of active faults—can be measured.Results show that the maximal migration distance of active faults at the NMAR is 0.76–1.01 km(the distance is larger at the center than at the ends of this segment),and at the NCR,the distribution range of active faults is 0.38–1.6 km.The migration distance of active faults on the two study areas is positively related to the axial variation of magma supply.In the NCR study area,where there is an abundant magma input,the number of faults within a certain distance is mainly affected by the variation of lithospheric thickness.Here a large range of faulting clearly corresponds to a high proportion of magmatism to seafloor spreading near mid-ocean ridges(M)value,and in the study area of the NMAR,there is insufficient magmatism,and the number of faults may be controlled by both lithospheric thickness and magma supply,leading to a less obvious positive correlation between the distribution range of active faults and M.

Damage quantitative assessment of spacecraft in a large-size inspection

To ensure the safety and reliability of spacecraft during multiple space missions,it is necessary to conduct in-situ nondestructive detection of the spacecraft to judge the damage caused by the hypervelocity impact of micrometeoroids and orbital debris(MMOD).In this paper,we propose an innovative quantitative assessment method based on damage reconstructed image mosaic technology.First,a Gaussian mixture model clustering algorithm is applied to extract images that highlight damage characteristics.Then,a mosaicking scheme based on the ORB feature extraction algorithm and an improved M-estimator SAmple Consensus(MSAC)algorithm with an adaptive threshold selection method is proposed which can create large-scale mosaicked images for damage detection.Eventually,to create the mosaicked images,the damage characteristic regions are segmented and extracted.The location of the damage area is determined and the degree of damage is judged by calculating the centroid position and the perimeter quantitative parameters.The efficiency and applicability of the proposed method are verified by the experimental results.

Quantitative Assessment Model on Path Assignment for SDNs

Software defined networking(SDN) has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a successful carrier grade network with SDN, in terms of scalability, reliability, Qo S and service management. In this paper, graph theory and traffic engineering are used to model the network state transitions and path assignment problem at first. Next, we present a quantitative assessment model on path assignment for a centralized controller to assess different kinds of path assignments, considering optimal path utilization, network load balance, network load volatility, and resource utilization simultaneously. In the end, an example forquantitatively assessing existing different path assignments is detailed to illustrate our proposed model.

Quantitative Risk Assessment with Data Mining for Formaldehyde Content of Imported Textile

The assessment of potential risks posed by formaldehyde in clothing to consumers is of increasing concern worldwide,for free formaldehyde is harmful to human. Some papers focus on hazard and exposure assessment,using quantitative and semi-quantitative method, rather than assessment for the factors related to formaldehyde content. In order to perform early-warning research and risk management,the quantitative risk assessment is used to analyze the imported textile testing data and find the factors related to formaldehyde content. K-means algorithm is the core of the method. It can be concluded that the disqualified textiles have relations with main fiber components,fabric color,season month,and brand. Then the data in the same attribute are clustered by Kmeans algorithm. The different classes are defined as related danger level,respectively severe,moderate,and light.

Risk of Waterborne Parasitic Infection among Vegetables Producers in the City of Ouagadougou, Burkina Faso: An Attempt to Quantify Them Using the Quantitative Microbiological Risk Assessment Method

The use of wastewater in urban agriculture has been promoted as an alternative to water scarcity and as a means to increase soil fertilizer. However, the use of wastewater in urban agriculture raises major public health concerns, mainly due to the often high concentration of pathogenic micro-organisms. Waterborne parasites are a major health concern in this regard, especially in endemic areas, mainly due to the high environmental resistance of eggs/cysts combined with a low infectious dose. In this study, the parasitological risk to vegetables producers in Ouagadougou using polluted water was evaluated through the quantitative microbiological risk analysis method. For this purpose, the search for and quantification of viable parasite eggs and cysts in irrigation water was coupled with epidemiological surveillance of vegetables producers in Ouagadougou. Protozoa and helminths belonging to 9 species were recorded and samples analyzed. These include Ancylostoma sp., Ascaris lumbricoides, tapeworm’s sp, Strongyloides stercoralis, Entamoeba histolytica, Giardia lamblia. Despite the variability of isolated parasitic species, Ancylostoma sp. eggs were common and severe to all types of water. Moreover, epidemiological approach showed that there were more parasitic species found in irrigation water and also other parasitic species circulating among vegetable farmers. In addition, there is no statistical significant association between the type of water used for irrigation and the carried parasites by vegetable gardeners. However, contamination of the groups using the raw wastewater is once higher than those using well water. When one is interested in the groups using treated wastewater as a source of irrigation, these risks of contaminations are halved. These contaminations are halved when one looks at groups using treated wastewater as a source of irrigation water. Still, whatever the type of water used by the gardeners, they have a probability of infection with Ancylostoma sp. of around 9.83 × 10−1 pppy.

Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

Cosmetic Safety and Risk Assessment Under the “New” Regulations

Regulations for the Supervision and Administration of Cosmetics was promulgated by the State Council on June 29,2020 and will take effect on January 1,2021.Compared with the previous Regulations on Health Supervision of Cosmetics,there are more items proposed for the safety of cosmetics.In this paper,the main changes of the safety supervision of cosmetics by comparing the“new”and“old”regulations were summarized with the potential influences on different groups(government,cosmetic enterprises and consumers)discussed.The important laws and regulations on cosmetic safety and risk assessment in China with recently added in vitro testing methods were summarized and the principal theory of cosmetic quantitative risk assessment was introduced.

Research progress of magnetic resonance imaging on the correlation between coronary plaque properties and pericoronal fat

Because of the small size of coronary plaque and its continuous beating with the heart,the assessment of the nature of coronary atherosclerotic plaques by magnetic resonance imaging remains a challenge in current research.Recent studies have shown that changes in the pericoronary fat environment are closely related to the nature of coronary atherosclerotic plaques.At present,the new multi-modal magnetic resonance technology can quantitatively assess the changes of pericorral fat environment,which is expected to effectively evaluate the properties of coronary artery plaque.Therefore,it is of greater clinical importance to study pericoronary fat.Advances in the biological significance of pericoronary fat,comparative imaging for pericoronary fat assessment and clinical applications of its imaging will be reviewed.

Utilizing Dynamic Data to Quickly Evaluate Advantageous Channels and Applications

In response to the high cost and difficulty of high-speed development and testing data in offshore oil fields, this paper proposes to use the most easily available production performance data as the basis and use the grey correlation method to calculate the correlation coefficient between oil and water wells to characterize the degree of development of advantageous channels. The consistency between the calculated results of this method and the tracer test results is over 80%. Based on the fitting results, the correlation coefficient exceeds 0.74 to determine the existence of an advantageous channel. According to the research results of grey correlation method, Bohai K oilfield has completed the combined profile control and flooding measures, and the daily oil production has increased by 20 m3/d. This method is simple, fast, and can achieve quantitative evaluation, which saves time and investment compared to offshore testing. It has strong application and reference value for the development of other offshore water injection oilfields.

Assessing the effects of vegetation and precipitation on soil erosion in the Three-River Headwaters Region of the Qinghai-Tibet Plateau,China

Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considered to be the main factors for the variation in soil erosion.However,it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale.To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015,we employed the Revised Universal Soil Loss Equation(RUSLE)model to evaluate soil erosion in the TRHR,and then developed a method using the Logarithmic Mean Divisia Index model(LMDI)which can exponentially decompose the influencing factors,to calculate the contribution values of the vegetation cover factor(C factor)and the rainfall erosivity factor(R factor)to the variation of soil erosion from the pixel scale.In general,soil erosion in the TRHR was alleviated from 2005 to 2015,of which about 54.95%of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor,and 41.31%was caused by the change in the R factor.There were relatively few areas with increased soil erosion modulus,of which 64.10%of the area where soil erosion increased was caused by the change in the C factor,and 23.88%was caused by the combined effects of the C factor and the R factor.Therefore,the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion,while the C factor was the dominant factor for the increase of soil erosion.The area with decreased soil erosion caused by the C factor(12.10×10^3 km^2)was larger than the area with increased soil erosion caused by the C factor(8.30×10^3 km^2),which indicated that vegetation had a positive effect on soil erosion.This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion,and also provided a scientific basis for the regional control of soil erosion.

Computed tomography perfusion imaging evaluation of angiogenesis in patients with pancreatic adenocarcinoma

BACKGROUND Pancreatic adenocarcinoma is one of the most common malignant tumors of the digestive system.More than 80%of patients with pancreatic adenocarcinoma are not diagnosed until late stage and have distant or local metastases.AIM To investigate the value of computed tomography(CT)perfusion imaging in the evaluation of angiogenesis in pancreatic adenocarcinoma patients.METHODS This is a retrospective cohort study.Patients with pancreatic adenocarcinoma and volunteers without pancreatic diseases underwent CT perfusion imaging from December 2014 to August 2017 in Huashan Hospital,Fudan University Shanghai,China.RESULTS A total number of 35 pancreatic adenocarcinoma patients and 33 volunteers were enrolled.The relative blood flow(r BF),and relative blood volume(r BV)were significantly lower in patients with pancreatic adenocarcinoma than in the control group(P<0.05).Conversely,the relative permeability in patients with pancreatic adenocarcinoma was significantly higher than that in controls(P<0.05).In addition,r BF,r BV,and the vascular maturity index(VMI)were significantly lower in gradeⅢ-Ⅳpancreatic adenocarcinoma than in gradeⅠ-Ⅱpancreatic adenocarcinoma(P<0.05).Vascular endothelial growth factor(VEGF),CD105-MVD,CD34-MVD,and angiogenesis rate(AR)were significantly higher in gradeⅢ-Ⅳpancreatic adenocarcinoma than in gradeⅠ-Ⅱpancreatic adenocarcinoma(P<0.05).Significant correlations between r BF and VEGF,CD105-MVD,AR,and VMI(P<0.01)were observed.Moreover,the levels of r BV were statistically significantly correlated with those of VEGF,CD105-MVD,CD34-MVD,and VMI(P<0.01).CONCLUSION Perfusion CT imaging may be an appropriate approach for quantitative assessment of tumor angiogenesis in pancreatic adenocarcinoma.

Available Contents and Spatial Distribution of Soil Microelements of Tobacco Fields in Liupanshui City

The contents of soil microelements can affect the growth, yield and quality of tobacco, but it is not clear of the status quo of soil microelements in tobacco fields in Liupanshui city. In this study, soil samples of the plough layers (0 - 20 cm) were collected from January to March in 2021 from 500 typical tobacco fields in Liupanshui City (100, 180 and 220 fields in Zhongshan, Shuicheng and Panzhou, respectively), the particle size composition, pH, the contents of organic matter (OM), available boron (B), copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo) and zinc (Zn) were measured and discussed. the results show that the mean contents of B, Cu, Fe, Mn, Mo and Zn are 0.92, 2.55, 66.47, 47.26, 0.39 and 3.96 mg/kg, respectively, and among which, B, Cu and Zn are at the high grades while Fe, Mn and Mo are at the very-high grades. There are significant differences in B, Fe and Zn among the three regions, while Cu is significantly different between Zhongshan and Shuicheng, Mn is significantly different between Panzhou with Zhongshan and Shuicheng, and Mo is significantly different between Zhongshan and Panzhou. The proportions of fields deficient in microelements are relatively low, no field is Fe-deficient, and the fields deficient in Mo, B, Cu, Zn and Mn are only account for 10.40%, 9.40%, 7.40%, 2.80% and 2.00% of the total fields respectively. B-deficient fields (green and light green colors, the same below) are mainly scattered in the northwest of Zhongshan and in the north and south of Panzhou, Cu-deficient fields mainly in the middle of Shuicheng and Panzhou, Mn-deficient fields mainly in northern Zhongshan, central Shuicheng and north of Panzhou. Mo-deficient fields are mainly in the middle of Shuicheng and the middle and east of Panzhou, while Zn-deficient fields are mainly in the middle of Shuicheng and the middle and east of Panzhou. Altitude is negatively correlated with Mn (P < 0.05), pH is negatively correlated with B (P < 0.05), Fe and Mn (P < 0.01), and OM is positively correlated with Fe and Zn (P < 0.01). Sand is negatively correlated with B (P < 0.05), Mn and Mo (P < 0.01), but positively correlated with Zn (P < 0.01). Silt is positively correlated with Mo (P < 0.05);clay is positively correlated with B (P < 0.05) and Mn (P < 0.01), but negatively correlated with Zn (P < 0.01). In conclusion, most of the fields do not need to apply micro-fertilizers in Liupanshui, but the fields deficient in microelements should be considered to use the corresponding fertilizers.

Digital Geosciences and Quantitative Mineral Exploration

The idea of mineral exploration,which is called"exploration philosophy"in the Western countries,is the thoughts,the methodology,technology,goals and organization that guide mineral exploration.The three basic elements of mineral exploration are"what to find","where to find"and"how to find".The concept of mineral exploration is gradually changing with the development of these three elements that provide a powerful driving force to change mineral exploration concepts,methods and technology.Innovation of mineral exploration concepts is the result of continuing exploration and development keeping pace with the times.The combination of"mathematical geology"and"information technology"can be called"digital geology".Digital geology is the data analysis component of geological science.Geological data science is a science that uses the general methodology of data to study geology based on the characteristics of geological data and the needs of geological field work.Digital mineral exploration is the application of digital geology in mineral exploration to reduce ore-finding uncertainty.

Assessment of terrace gardens as modifiers of building microclimate

Hard concrete roofs cause excessive heat gain impacting thermal comfort in buildings.Terrace gardens promoting greening at higher levels of built structures are seen as one of the key mitigating strategies for modifying building microclimate and improving urban health.We have undertaken a research project to quantitatively assess the value of a terrace garden in a residential scale.A garden patch of about 15 m 2 in area,a typical size available in most urban terraces is developed.Surface temperatures are measured over a period of 15 months between July 2018 to January 2020 using four thermocouple sensors,placed within and outside the garden bed,on and below the roof.We compare the thermal performance of the terrace garden across years,seasons,time of the day,presence or absence of garden bed and type and height of vegetation.The surface temperature data was found to correlate well with the ambient air temperature values.The results show that the terrace garden moderates and stabilises the ceiling temperatures and reduces it by about 2-3°C in winter months and 5-7°C in hot summers.The garden also provided nearly 400 g of fresh monthly vegetable harvest per m 2 of garden.Further,the cooling impact of the terrace garden with natural,tall,wild vegetation is higher as compared to planted vegetation.The study demonstrates a sustainable approach to terrace garden design at residential scale through quantified dual benefits of temperature control within buildings and urban farming.

Atmospheric dispersion of chemical,biological,and radiological hazardous pollutants:Informing risk assessment for public safety

Modern society is confronted with emerging threats from chemical,biological,and radiological(CBR)hazardous substances,which are intensively utilized in the chemical,medical,and energy industries.The atmospheric dis-persion of released CBR hazardous pollutants can influence a large percentage of the population owing to their rapid process with extensive spatial coverage.It is important to comprehensively understand the behaviors of the released CBR pollutants in the atmosphere to fully evaluate the risks and protect public safety.In this study,we reviewed the advancements in the atmospheric transport of CBR pollutants,including the urban atmospheric boundary layer,unique concepts,and models for CBR pollutants.We underlined the development of innovative methodologies(e.g.,inverse estimation and data assimilation methods)for the atmospheric transport of acciden-tally released CBR pollutants to reduce uncertainties in emissions and accumulated errors during dispersion by combining numerical models with monitoring data.Finally,we introduced progress in quantitative risk assess-ment,including exposure assessment and dose-response relationships for CBR hazardous pollutants.A framework,source,assimilation,fundamentals,exposure,and risk(SAFER),has been proposed to integrate the key compo-nents in the risk assessment of airborne CBR hazardous pollutants.These methods and models can contribute to effective risk preparedness,prevention,evidence-based policymaking,and emergency response to airborne CBR pollutants.