Efficient Manner
Recently Published Documents





2022 ◽  
Vol 2022 ◽  
pp. 1-20
Harish Garg ◽  
Zeeshan Ali ◽  
Ibrahim M. Hezam ◽  
Jeonghwan Gwak

A strategic decision-making technique can help the decision maker to accomplish and analyze the information in an efficient manner. However, in our real life, an uncertainty will play a dominant role during the information collection phase. To handle such uncertainties in the data, we present a decision-making algorithm under the single-valued neutrosophic (SVN) environment. The SVN is a powerful way to deal the information in terms of three degrees, namely, “truth,” “falsity,” and “indeterminacy,” which all are considered independent. The main objective of this study is divided into three folds. In the first fold, we state the novel concept of complex SVN hesitant fuzzy (CSVNHF) set by incorporating the features of the SVN, complex numbers, and the hesitant element. The various fundamental and algebraic laws of the proposed CSVNHF set are described in details. The second fold is to state the various aggregation operators to obtain the aggregated values of the considered CSVNHF information. For this, we stated several generalized averaging operators, namely, CSVNHF generalized weighted averaging, ordered weighted average, and hybrid average. The various properties of these operators are also stated. Finally, we discuss a multiattribute decision-making (MADM) algorithm based on the proposed operators to address the problems under the CSVNHF environment. A numerical example is given to illustrate the work and compare the results with the existing studies’ results. Also, the sensitivity analysis and advantages of the stated algorithm are given in the work to verify and strengthen the study.

Hyundong Nam ◽  
Taewoo Nam ◽  
Minjeong Oh ◽  
Sungyong Choi

Information and Communications Technology (ICT) network readiness competency improves service quality and provides efficient service in implementing successful e-governments. By confirming ICT network readiness of e-governments, it must be redesigned using limited resources effectively to achieve realistic goals. When ICT investment and economic performance are featured, e-government’s network readiness competency improves potential demand, supply, and service maturity. It reflects information technology (IT) development competency on performance effectively. In this study, we propose the Data Envelope Analysis (DEA) method to present a method of improving ICT network readiness between countries. We derived the ICT network’s readiness competency level and strategic plan by comparing each country for efficient ICT operation of e-governments. If we make rankings in a non-traditional and efficient manner, it will become a successful strategy for ICT in the future. This effort provides guidance for each government and a solution for the growth delay problem, which is required for advancement in ICT investment and productivity. It also guides each government to overcome marginal products.

Kourosh Khadivi ◽  
Mojtaba Alinaghi ◽  
Saeed Dehghani ◽  
Mehrbod Soltani ◽  
Hamed Hassani ◽  

AbstractThe Asmari reservoir in Haftkel field is one of the most prolific naturally fractured reservoirs in the Zagros folded zone in the southwest of Iran. The primary production was commenced in 1928 and continued until 1976 with a plateau rate of 200,000 bbl/day for several years. There was an initial gas cap on the oil column. Gas injection was commenced in June 1976 and so far, 28% of the initial oil in place have been recovered. As far as we concerned, fracture network is a key factor in sustaining oil production; therefore, it needs to be characterized and results be deployed in designing new wells to sustain future production. Multidisciplinary fracture evaluation from well to reservoir scale is a great privilege to improve model’s accuracy as well as enhancing reliability of future development plan in an efficient manner. Fracture identification and modeling usually establish at well scale and translate to reservoir using analytical or numerical algorithms with the limited tie-points between wells. Evaluating fracture network from production data can significantly improve conventional workflow where limited inter-well information is available. By incorporating those evidences, the fracture modeling workflow can be optimized further where lateral and vertical connectivity is a concern. This paper begins with the fracture characterization whereby all available data are evaluated to determine fracture patterns and extension of fracture network across the field. As results, a consistent correlation is obtained between the temperature gradient and productivity of wells, also convection phenomenon is confirmed. The findings of this section help us in better understanding fracture network, hydrodynamic communication and variation of temperature. Fracture modeling is the next step where characteristics of fractures are determined according to the structural geology and stress directions. Also, the fault’s related fractures and density of fractures are determined. Meanwhile, the results of data evaluation are deployed into the fracture model to control distribution and characteristics of fracture network, thereby a better representation is obtained that can be used for evaluating production data and optimizing development plan.

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 99
Nikolaos Malamas ◽  
Konstantinos Papangelou ◽  
Andreas L. Symeonidis

Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed “healthcare vocabulary” to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor’s appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant’s behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.

2021 ◽  
Minchul Kang

Abstract In most biological processes, diffusion plays a critical role in transferring various bio-molecules to transfer desirable locations in an effective and energy-efficient manner. How fast molecules are transferred is measured by diffusion coefficients. Since each bio-molecules, in particular, signaling molecules have their unique diffusion coefficients and quantifying the diffusion coefficients help us to understand various time scales of both physiological and pathological processes in biological systems. Moreover, since diffusion profiles of a diffusant vary in different micro-environments of cell membranes, accurate diffusion coefficient also can provide a good picture of membrane landscapes as well as interactions of different membrane constituents. Currently, only a few experimental methods are available to assess the diffusion coefficient of a biomolecule of interest in live cells including Fluorescence Recovery After Photobleaching (FRAP). FRAP was developed to study diffusion processes of biomolecules in the cell membranes in the 1970s. Albeit its long history, the main principle of FRAP analysis has remained unchanged since its inception: fitting FRAP data to a theoretical diffusion model for the best fitting diffusion coefficient or using the relation between the half time of recovery and ROI size. In this study, we developed a flexible yet versatile confocal FRAP data analysis framework based on linear regression analysis which allows FRAP users to determine the diffusion from either single or multiple FRAP data points without data fitting. We also validated this approach for a series of fluorescently labeled soluble and membrane-bound proteins and lipids.

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 275
Casper Bak Pedersen ◽  
Kasper Gaj Nielsen ◽  
Kasper Rosenkrands ◽  
Alex Elkjær Vasegaard ◽  
Peter Nielsen ◽  

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.

Musa Midila Ahmed

Purpose: E-government is a means of providing services by facilitating communication between government and citizens, government and businesses and among levels of government in a robust, flexible and efficient manner. Service-Oriented Software Engineering (SOSE) is a recognized approach of developing robust, flexible and efficient E-government system by transformation of existing systems. However, several challenges and issues in SOSE-Based E-government systems need to be addressed to attend the desired goal. This paper used systematic literature review (SLR) to identify, classify and analyze primary studies published in the research community from 2010 to 2021 that focused on SOSE approach for E-government systems. Methodology/Approach: A review protocol was applied to gather 61 relevant primary studies that were critically analyzed, classified and evaluated. Findings/Result: The results shows that research was active on this topic within the reviewed period. The review’s record shows that the highest number of publication of 10 was in 2010 and lowest number of publication of 2 was in 2016. Furthermore, interoperability received the highest attention by researchers with 22 publications, whereas security received the lowest attention with 2 publications in the reviewed period. It is recommended that further research be conducted on modelling in view of the heterogeneous and dynamic nature of business operations in governance. In addition, further research is required toward SOSE security for protection of E-government systems. Originality/Value: A new research direction identified on SOSE-Based E-Government System Paper Type: Review Research Paper.

Daniel MBURASEK ◽  

Efficient team formation presents challenges both for the industry and the academia, especially among first year students. In academia, the difficulty is due to a lack of familiarity between instructors and new students at the beginning of each semester while in the industry, the issue is the incomplete picture of new employee’s personality by the supervisors. The quality of the team greatly affects both the team member experience as well as the outcome of assigned projects. There is a strong need to create a tool or a program that allows instructors and supervisors to create effective teams with evenly distributed skills amongst the teams in a timely fashion. Studies show that the balance of skills, rather than the presence of highly skilled individuals, leads to successful teams. The ultimate goal is to create a tool that will give teams the opportunity to operate at their maximum potential. This paper focuses on the creation of teams for first year students of engineering. The outcome is based on the results of a project assigned to a team of second year engineering students. The choice of second year students was dictated by the need to have students who had already experienced the adverse effects of malfunctioning teams during their previous projects. The goal of the project was to design a software and user interface for a tool that instructors could use to create optimal project teams in an efficient manner.

2021 ◽  
Annika Faucon ◽  
Julian Samaroo ◽  
Tian Ge ◽  
Lea K Davis ◽  
Ran Tao ◽  

To enable large-scale application of polygenic risk scores in a computationally efficient manner we translate a widely used polygenic risk score construction method, Polygenic Risk Score – Continuous Shrinkage (PRS-CS), to the Julia programing language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average run time by 5.5x. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes utilization of polygenic risk scores by lowering the computational burden of the PRS-CS method.

2021 ◽  
Vol 3 (4) ◽  
pp. 367-376
Yasir Babiker Hamdan ◽  
A. Sathesh

Due to the complex and irregular shapes of handwritten text, it is challenging to spot and recognize the handwritten words. In low-resource scripts, retrieval of words is a difficult and laborious task. The need for increasing the number of samples and introducing variations in the extended training datasets occur with the use of deep learning and neural network models. All possible variations and occurrences cannot be covered in an efficient manner with the use of the existing preprocessing strategies and theories. A scalable and elastic methodology for wrapping the extracted features is presented with the introduction of an adversarial feature deformation and regularization module in this paper. In the original deep learning framework, this module is introduced between the intermediate layers while training in an alternative manner. When compared to the conventional models, highly informative features are learnt in an efficient manner with the help of this setup. Extensive word datasets are used for testing the proposed model, which is built on popular frameworks available for word recognition and spotting, while enhancing them with the proposed module. While varying the training data size, the results are recorded and compared with the conventional models. Improvement in the mAP scores, word-error rate and low data regime is observed from the results of comparison.

Sign in / Sign up

Export Citation Format

Share Document