scholarly journals Neutrosophic statistical analysis of resistance depending on the temperature variance of conducting material

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Usama Afzal ◽  
Hleil Alrweili ◽  
Naveed Ahamd ◽  
Muhammad Aslam

AbstractIn this work, we have proposed a neutrosophic statistical approach for the analysis of resistance of conducting material depending on the temperature variance. We have developed a neutrosophic formula and applied it to the resistance data. We also use the classical statistical approach for making a comparison between both approaches. As a result, it is observed that the neutrosophic statistical approach is more flexible and informative. Also, this work suggests that the neutrosophic statistical approach analyzes the resistance of conducting material for big data.

2021 ◽  
Vol 5 (2) ◽  
pp. 369
Author(s):  
Shasliani Shasliani

This study aims, namely, 1) to determine how the method of group work in social studies subjects at SD Inpres Kampus IKIP, 2) to determine whether the technique of group work affects improving student learning outcomes in social studies topics. The research was conducted using a quantitative statistical approach. The data collection techniques used were observation, questionnaires, interviews, and documentation. The data were analyzed using descriptive statistical analysis and quantitative statistical analysis. Based on the research results, it can be seen that 1) the application of group work methods in social studies subjects at SD Inpres Kampus IKIP Makassar city is in the "good" category with indicators of fostering interest and the ability to cooperate among students, increasing socio-emotional involvement of students and increasing attention on the process and results of the learning process, 2) the application of group work methods affects improving student learning outcomes in social studies subjects at SD Inpres IKIP Kampus, Makassar city.


Author(s):  
Boris Yurievich Lemeshko ◽  
◽  
Stanislav Borisovich Lemeshko ◽  
Mariya Alexandrovna Semenova

2021 ◽  
Author(s):  
M. Hatta M. Yusof ◽  
M. Zarkashi Sulaiman ◽  
Rahimah A. Halim ◽  
Nurfaridah Ahmad Fauzi ◽  
Ahgheelan Sella Thurai ◽  
...  

Abstract This paper discusses the Case study of Field A in offshore Sarawak, Malaysia which focus on re-thinking development based on statistical analysis of the fields. Conventionally, well design is driven by subsurface requirement by targeting the high-reserve sand and well is designed to meet subsurface objectives. However, the conventional way may not be efficient to develop matured field environment due to the high CAPEX and the inconsistencies among well design especially in current volatile oil price period. The objective of this fit-for-purpose approach which is called "Cone Concept Statistical Approach" is to steer away from the conventional way of targeting only sweet spots whilst leaving the remaining potential resources undeveloped. Based on the statistical analysis and subsurface fields pattern, the "Cone Concept Statistical Approach" in which standardizing well design and trajectories was developed to extract the whole fields’ reserve at maximum. Well design boundaries were introduced to ensure this approach can be replicated throughout the field. Not only this study covers drilling perspective, completion perspective was also taken into consideration by exploring a cheaper and fit for purpose sand control method, considering it is a matured field with relatively short remaining field life. The Well Cost Catalogue for this field-specific approach was also developed which contains different types of design and completion, in order to holistically evaluate sand control method and identify the best option for the project moving forward. This "Cone Concept Statistical Approach" aims to enable operator to drill simple wells within the same allocated budget in which poses low-to-no risk in the design and execution phase. This promotes a learning curve to improve operation & HSE, and ultimately gets positive project economics. Since this simple approach can be implemented early on even during the pre-FEL stage, the FDP team & host authority can come together to jointly discuss the targets/platform ranking and segregate them into various phases. Hence, the number of platforms or drilling centers, and its location also can be optimized early on with this concept, and again, translating into further reduction in overall project cost. This paper will help other operators and host authority to understand better on how a specific development concept on statistical approach can result and turn the matured-challenging fields into more economically attractive projects – low overall development cost and maximizing the recovery.


Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.


Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.


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