Identifying the Impact of Preconstruction Elements on Project Time Variances

Working toward an efficient duration and timeline for the preconstruction phase should be one of the main objectives for project owners.Failing to plan for and coordinate preconstruction decisions in order to control preconstruction duration and manage time variances can lead to financial insecurities,incomplete contract documents,permitting issues,and unrealistic schedules and resource allocation during this phase.To minimize time variances and ensure a productive decision-making process,project owners should be familiar with critical elements in a project that cause variances in the preconstruction phase timeline.In this study,the impacts of eleven critical preconstruction elements on time variances were analyzed.These eleven preconstruction elements are considered critical in how they impact time variances during the preconstruction phase.They were determined to be critical based either on significantly impacting time variance during the preconstruction phase or believed to be critical from findings from previous studies,however,the findings from this study showed no significant impact on the time variances.In most previous studies focusing on the elements impacting project schedules,data were collected by surveying construction professionals.In this study,objective and quantitative data related to project preconstruction elements were used as opposed to self-reported data.Using the results of this study,project owners and stakeholders will be able to evaluate the critical preconstruction elements impacting the timing of their projects and prioritize decisions related to the critical elements early on during the preconstruction phase.

Identifying Pathfinder Elements for Gold in Multi-Element Soil Geochemical Data from the Wa-Lawra Belt, Northwest Ghana: A Multivariate Statistical Approach

A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.

Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

Abstraction of Data Elements of Clinical Symptoms in Chinese Medicine

This report analyzes the existing problems in terminology referring to clinical symptoms of traditional Chinese medicine(TCM)from the viewpoint of data sharing and elaborates the necessity of establishing a standard directory of clinical data elements of TCM.We evaluated the principles and methods of data element extraction according to the status quo of the clinical information system and characteristics of symptoms for TCM and consequently proposed a three-layer model for optimal extraction.

Aviation Safety and Data Mining in Marketing Dimension

The aviation industry is a sector that is developing, changing and growing every day in terms of technological and legal framework. There are generally three factors that enable airlines to hold on to the market. These factors are safety, service quality and price. Airline companies can analyze the customers in the market with a focus on price and quality and develop a business model according to their expectations. For example, business class and economy class passenger expectations are different from each other, so the service and price to be offered to them will be different. However, all customers have one common expectation and that is safety. No matter how high quality the service is or how cheap the price is, no one wants to fly with an airline or plane that is not safe. From an airline company’s point of view, an accident or breakdown of one of the company’s aircraft can cause irreparable image loss and financial damage. If we look at past examples, we see that there are many airline companies or maintenance organizations that could not recover after an accident and went bankrupt. Safety is an indispensable factor. Therefore, there is a unit in the sector called the safety management system (SMS), which collects data by taking a proactive and reactive approach. The way and purpose of the safety management system is to take a proactive approach to recognize and prevent unsafe situations before they cause accidents or breakdowns, or to take a reactive approach to find the causes of accidents and breakdowns that have occurred as a result of certain factors and to take the necessary measures to prevent the same situations from happening again in the sector. The field of data mining, which is necessary to predict the future behavior of customers in the field of marketing, is an area that marketing also values. In this study, data mining studies to ensure safety in the aviation industry and the security of customer information in marketing will be emphasized, firstly, the concept and importance of data mining will be mentioned.

Thoughts About Paths for Digitization of Traditional Chinese Medicine-Refined Data Elements

For digitalization of traditional Chinese medicine(TCM),research is being conducted on objectivization of diagnosis and treatment,mathematical models of TCM theories,and application of modern information technology to digitize the vast amounts of existing information.However,the author believes that TCM practitioners should first conduct a systematic and comprehensive refined analysis on the knowledge of TCM and unify data elements used in computer intelligence to avoid ambiguity.Thus,we must overcome the epistemological constraints and carefully analyze the relationship among data elements to achieve systematic results and administer TCM appropriately.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Optimizing Data Collection Path in Sensor Networks with Mobile Elements

Exploiting mobile elements （MEs） to accomplish data collection in wireless sensor networks （WSNs） can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps： we first construct a virtual grid network and select the minimal stop point set （SPS） from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm （GA） are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.

Exploring the Pathways of Power Grid Marketing Management Based on Big Data

The electric power industry is undergoing profound transformations driven by big data,posing challenges to the traditional power grid marketing management model.These challenges include neglecting market demands,insufficient data support,and inadequate customer service.The application of big data technology offers innovative solutions for power grid marketing management,encompassing critical aspects such as data collection and integration,storage management,analysis,and mining.By leveraging these technologies,power grid enterprises can precisely understand customer needs,optimize marketing strategies,and enhance operational efficiency.This paper explores strategies for power grid marketing management based on big data,addressing areas such as customer segmentation and personalized services,as well as market demand forecasting and response.Furthermore,it proposes implementation pathways,including essential elements such as organizational structure and team building,data quality and governance systems,training,and cultural development.These efforts aim to ensure the effective application of big data technology and maximize its value.

What Are the Key Areas of the“Data Elements X”Three-year Plan?

The first plan on data elements since the listing of the National Bureau of Data of China has been released.On December 15th,2023,the National Bureau of Data of China drafted the Three-year Plan for“Data Elements X”(2024-2026)(Exposure Draft),and solicited opinions from the public.

Design of the Testing System for Jet Elements

To analyze the errors of processing data, the testing principle for jet elements is introduced and the property of testing system is theoretically and experimentally studied. On the basis of the above, the method of processing data is presented and the error formulae, which are the functions of the testing system property, are derived. Finally, the methods of reducing the errors are provided. The measured results are in correspondence with the theoretical conclusion.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Model of generic project risk element transmission theory based on data mining

In order to construct the data mining frame for the generic project risk research, the basic definitions of the generic project risk element were given, and then a new model of the generic project risk element was presented with the definitions. From the model, data mining method was used to acquire the risk transmission matrix from the historical databases analysis. The quantitative calculation problem among the generic project risk elements was solved. This method deals with well the risk element transmission problems with limited states. And in order to get the limited states, fuzzy theory was used to discrete the historical data in historical databases. In an example, the controlling risk degree is chosen as P(Rs≥2) ≤0.1, it means that the probability of risk state which is not less than 2 in project is not more than 0.1, the risk element R3 is chosen to control the project, respectively. The result shows that three risk element transmission matrix can be acquired in 4 risk elements, and the frequency histogram and cumulative frequency histogram of each risk element are also given.

Finite Element Simulation of Flexible Roll Forming with Supplemented Material Data and the Experimental Verification

Flexible roll forming is a promising manufacturing method for the production of variable cross section products. Considering the large plastic strain in this forming process which is much larger than that of uniform deformation phase of uniaxial tensile test, the widely adopted method of simulating the forming processes with non-supplemented material data from uniaxial tensile test will certainly lead to large error. To reduce this error, the material data is supplemented based on three constitutive models. Then a finite element model of a six passes flexible roll forming process is established based on the supplemented material data and the original material data from the uniaxial tensile test. The flexible roll forming experiment of a B pillar reinforcing plate is carried out to verify the proposed method. Final cross section shapes of the experimental and the simulated results are compared. It is shown that the simulation calculated with supplemented material data based on Swift model agrees well with the experimental results, while the simulation based on original material data could not predict the actual deformation accurately. The results indicate that this material supplement method is reliable and indispensible, and the simulation model can well reflect the real metal forming process. Detailed analysis of the distribution and history of plastic strain at different positions are performed. A new material data supplement method is proposed to tackle the problem which is ignored in other roll forming simulations, and thus the forming process simulation accuracy can be greatly improved.

Tobacco control challenges in China: Big data analysis of online tobacco marketing information

Objective:To develop tobacco control strategies by analyzing online tobacco marketing information in China.Methods:Using web-crawler software,this study acquired 106,485 pieces of online tobacco marketing information published on 11 different Internet platforms including Weibo,WeChat,Baidu,etc.,from January-June 2018.The data were used to investigate the characteristics and social networks of online tobacco marketing via content and social network analysis.Results:The total volume of online tobacco marketing during the study period was high,showing a positive trend.Of all the marketing subjects,those involving"flavor capsule","Marlboro",and"Esse"were the most popular.The Weibo platform had the highest volume of online tobacco marketing information as well as the largest proportion of explicit marketing information.This was followed by other social media platforms such as Baidu Search,Baidu Tieba,and Xiaohongshu,where implicit marketing information predominated.The overall network structure of tobacco websites exhibited a significant centralization feature,where traditional and novel tobacco websites formed two clusters with almost no intersections.The China Tobacco Science and Education Website(http://www.tobaccoinfo.com.cn/)and E-Cigarette Home(http://ecigm.com/)were the two nodes of the highest degree centrality within the respective"circle",while the China Tobacco Monopoly Bureau Website(http://www.tobacco.gov.cn/)was the node with the highest closeness centrality.By contrast,Baidu Tieba's overall network structure was more decentralized,and the degree of correlation between different nodes was relatively low.Conclusion:Online tobacco marketing demonstrated high volumes and wide coverage,and an intertwined network,thereby creating major obstacles for tobacco control.To address this issue,the government should strengthen network supervision of tobacco marketing and revise its current regulations.Meanwhile,Internet platforms should improve self-regulation by comprehensively removing and blocking tobacco-related information.Lastly,the media and public should advocate associated policies and support Internet platform supervision.

Internet big data and capital markets:a literature review

Background:Research in various academic disciplines has undergone tremendous changes in the era of big data.Everyone is talking about big data nowadays,but how exactly is it being applied in research on financial studies?Results:This study summarizes the sources of Internet big data for research related to capital markets and the analytical methods that have been used in the literature.In addition,it presents a review of the research findings based on Internet big data in the field of capital markets and proposes suggestions for future studies in which big data can be applied to examine issues related to capital markets.Conclusion:(1)Internet big data sources related to present capital market research can be categorized into forum-type data,microblog-type data and search class data.(2)As for research about investors’sentiments on the basis of Internet big data,the main methods of sentiment analysis include building an inventory of lexical categories,using dictionaries for analysis of lexical categories,and machine learning.(3)Many studies address whether Internet big data can predict capital markets.However,they reach no consistent conclusions,which could be due to limitations of sample and analysis method used.(4)Data collection technique and analysis methods require further improvements.

Indian stock market prediction using artificial neural networks on tick data

Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.

Social Media and Stock Market Prediction: A Big Data Approach

Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns.The quantity and variety of computer data are growing exponentially for many reasons.For example,retailers are building vast databases of customer sales activity.Organizations are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and products.Challenges of big data include volume and variety in both structured and unstructured data.In this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional models.We studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision tree.We implemented naive Bayes and logistic regression classification models.Experimental results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

Big Data Usage in the Marketing Information System

Data generation, storage capacity, processing power and analytical capacity increase had created a technological phenomenon named big data that could create big impact in research and development. In the marketing field, the use of big data in research can represent a deep dive in consumer understanding. This essay discusses the big data uses in the marketing information system and its contribution for decision-making. It presents a revision of main concepts, the new possibilities of use and a reflection about its limitations.

The effects of land market development on economic growth: an empirical analysis based on Chinese panel data,1999-2005

Optimized land resources allocation is important for economic growth because land is one of the basic elements for economic development. And urban land resources allocation has had an increasingly important influence since the Chinese socialist market economy system was established. This paper estimates the production function of both the secondary and the tertiary industries of China＇s 31 provinces, autonomous regions and municipalities directly under the central government through an analysis of the panel data of the total output value of the secondary and the tertiary industries, invested capital, invested labor jorces and the land market-jeatured management of the above-mentioned regions during the period of 1999-2005. and examines the positive influence of the above- mentioned factors on regional economic output, This study concludes that urban economic output is positively related with the level of urban land resources market-featured management, since the rate of economic growth of those regions approximates 14. 7% under the condition of urban land market running during the period of 1999-2005.