Technology Forecasting of Unmanned Aerial Vehicle Technologies through Hierarchical S Curves

This study aims to propose a technology forecasting approach based on hierarchical S-curves. The proposed approach uses holistic forecasting by evaluating the S-curves of sub-technologies as well as the main technology under concern. A case study of unmanned aerial vehicle (UAV) technologies is conducted to demonstrate how the proposed approach works in practice. This is the first study that applies hierarchical S-curves to technology forecasting of unmanned aerial vehicle technologies in the literature. The future trend of the UAV technologies is analysed in detail through a hierarchical S-curve approach. Hierarchical S-curves are also utilised to investigate the sub-technologies of the UAV. In addition, the technology development life cycle of technology is assessed by using the three indexes namely, (1) the current technological maturity ratio (TMR), (2) estimating the number of potential patents that could be granted in the future (PPA), and (3) forecasting the expected remaining life (ERL). The results of this study indicate that the UAV technologies and their sub-technologies are at the growth stage in the technology life cycle, and most of the developments in UAV technology will have been completed by 2048. Hence, these technologies can be considered emerging technologies

An interaction-oriented multi-agent SIR model to assess the spread of SARS-CoV-2

It is important to recognize that the dynamics of each country are different. Therefore, the SARS-CoV-2 (COVID-19) pandemic necessitates each country to act locally, but keep thinking globally. Governments have a responsibility to manage their limited resources optimally while struggling with this pandemic. Managing the trade-offs regarding these dynamics requires some sophisticated models. "Agent-based simulation" is a powerful tool to create such kind of models. Correspondingly, this study addresses the spread of COVID-19 employing an interaction-oriented multi-agent SIR (Susceptible Infected-Recovered) model. This model is based on the scale-free networks (incorporating 10,000 nodes) and it runs some experimental scenarios to analyze the main effects and the interactions of "average-node-degree", "initial-outbreak-size", "spread-chance", "recovery-chance", and "gain-resistance" factors on "average-duration (of the pandemic last)", "average-percentage of infected", "maximum-percentage of infected", and "the expected peak-time". Obtained results from this work can assist determining the correct tactical responses of partial lockdown

Decision making in the manufacturing environment using the technique of precise order preference

Wrong decisions in manufacturing systems can jeopardize the continuity of production and reduce productivity and efficiency. The ref ore, it is ess ential to mak e the rig ht dec isions in solving the problems encountered in manufacturing environments. In the literature, there are many methods developed to be used in solving decision-making problems. The results of different methods used in solving the same problem are different from each other. Thus, the rankings obtained by the different methods to solve the same decision-making problem in the manufacturing environment are different. Different rankings obtained for the same problem cause inconsistencies and it is not easy to determine which sort of order is better. In this study, the use ofthe technique ofprecise order preference (TPOP) is proposed to solve the decision-making problems in manufacturing systems. Three case studies a re p resented t o illustrate the use o f the TPOP method to solve decision-making problems in manufacturing systems. The c ase studies show that the TPOP method can be used easily to solve decision-making problems in manufacturing systems. Furthermore, the consistencies of the multi-criteria decision-making methods used in this study are analyzed using Spearman's correlation coefficient values. TPOP method has the highest Spearman's correlation value for three case studies

Evaluation of service quality using SERVQUAL scale and machine learning algorithms: a case study in health care

Purpose This study aims to propose a service quality evaluation model for health care services. Design/methodology/approach In this study, a service quality evaluation model is proposed based on the service quality measurement (SERVQUAL) scale and machine learning algorithm. Primarily, items that affect the quality of service are determined based on the SERVQUAL scale. Subsequently, a service quality assessment model is generated to manage the resources that are allocated to improve the activities efficiently. Following this phase, a sample of classification model is conducted. Machine learning algorithms are used to establish the classification model. Findings The proposed evaluation model addresses the following questions: What are the potential impact levels of service quality dimensions on the quality of service practically? What should be prioritization among the service quality dimensions and Which dimensions of service quality should be improved primarily? A real-life case study in a public hospital is carried out to reveal how the proposed model works. The results that have been obtained from the case study show that the proposed model can be conducted easily in practice. It is also found that there is a remarkably high-service gap in the public hospital, in which the case study has been conducted, regarding the general physical conditions and food services. Originality/value The primary contribution of this study is threefold. The proposed evaluation model determines the impact levels of service quality dimensions on the service quality in practice. The proposed evaluation model prioritizes service quality dimensions in terms of their significance. The proposed evaluation model finds out the answer to the question of which service quality dimensions should be improved primarily

The evaluation of occupational accident with sequential pattern mining

Accidents in manufacturing systems greatly affect productivity and efficiency, which are well known perfor-mance indicaters in practice. Therefore, it is very important to know the sequential patterns among the accidents to avode possible losses decrasing performance of the manufacturing systems. In order to reduce accidents, it is necessary to determine the patterns that cause the accident first. The associations among the causes of the occurrence of accidents is rarely investigated in the literature. To fill this gap, the patterns of causes among the accidents in the manufacturing system are revealed by using sequential pattern mining in this study. The most important contribution of this study is the discovery of sequential patterns formed by accident characteristics of pre-accident, moment of accident and post-accident stages unlike traditional accident investigation methods. Additionally, knowing the patterns of causes among the accidents can help decision makers to prepare a more proactive security program in real life. The CloFast algorithm is performed to go into the details of accidents in manufacturing systems. Accident records induding data between 2013 and 2019 are used to discover the sequential patterns. The results of this study showed that each accidents has its own sequential accident patterns and it is also posible to prevent possible accidents and reduce losses due to accidents considering sequential patterns in real life. Safety engineers and occupational safety specialists should take into account the sequential patterns among the accidents to avoid similar accident in the near future

Fuzzy dematel method to evaluate the dimensions of marketing resources: An application in SMEs

Identifying the cause and effect factors of marketing resources and prioritizing them with respect to their level of importance can build superior market performance for companies. Although there have been some studies in the literature which have used marketing resource dimensions to conduct their research, these studies have not considered the relationships between marketing resource dimensions. Therefore, the aim of this study is to identify the cause and effect factors of marketing resources and to prioritize them in terms of their importance using the fuzzy Decision-Making Trial and Evaluation Laboratory method. The findings of this study suggest that the dimension managerial capabilities, composed of financial management, effective human resource managementand good operations management expertise, exerts a greater influence on marketing strategy than other criteria. In addition, the criterion credibility with customers through being well established in the market is the most important aspect of marketing resources

Comparing the innovation performance of EU candidate countries: an entropy-based TOPSIS approach

Innovation is important for countries in the competitive global economy. It is one of the main criteria for countries to be superior, to remain competitive, and to produce high technology products. Countries allocate different types of incentives to encourage innovation activities in their countries. Innovation is also one of the strategic issues for the European Union (EU). The aim of this study is to compare the innovation performance of four EU candidate countries, Macedonia (FYR), Iceland, Serbia and Turkey. The entropybased Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach is proposed in this paper. First, the importance of each variable is computed by the entropy method to reflect on the differences among the variables in the calculation process. Subsequently, the TOPSIS method is performed by using the value and importance of variables for prioritisation of the candidate countries with respect to their innovation performance. Four case studies are conducted to show the viability of the proposed approach. Each cases study uses different reports, namely The Global Competitiveness Index, Innovation Union Scoreboard, Knowledge Assessment Methodology (KAM) and Global Innovation Index. The results of this study show that the proposed approach provides the same ranking as Innovation Union Scoreboard and KAM

Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods

Excavators are quite expensive vehicles. Therefore, there may be huge losses for decision makers if a wrong decision is made during the purchasing process. A good evaluation of excavator alternatives both reduces costs and increases the benefits the excavator for the purchaser. The aim of this study is to prioritise excavator technologies to help decision makers during the purchasing process and to apply three different “data fusion methods” instead of the “theory of dominance” of the original MULTI MOORA method. The MULTI MOORA method is composed of three methods, namely: the ratio analysis as a part of MOORA, Reference Point Theory (the reference point approach as a part of MOORA) and the Full Multiplicative Form. It is used to prioritise excavator technologies in this study. The MULTI - MOORA method combines three results obtained from these three methods using the theory of dominance. Dominance directed graph, Rank position method and Borda count method as data fusion methods are also used to combine these three results instead of the “theory of dominance”. The results from this study show that there is no difference between the data fusion methods and the MULTI MOORA method can be applied to technology evaluation of the excavator alternatives successfully

Predicting patent quality based on machine learning approach

The investment budget allocated by companies in R&D activities has increased due to increased competition in the market. Applications for industrial property rights by countries, investors, companies, and universities to protect inventions obtained as an outcome of investments have also increased. The selection of the patent to be invested becomes more difficult with the increasing number of applications. Therefore, predicting patent quality is quite significant for companies to be successful in the future. The level to which a patent meets the expectations of decision makers is referred to as patent quality. Patent indices represent decision makers' expectations. In this study, an approach is proposed to predict patent quality in practice. The proposed approach uses supervised learning algorithms and analytic hierarchy process (AHP) method. The proposed approach is applied to patents related to personal digital assistant technologies. The performances of individual and ensemble machine learning methods have been also analyzed to establish the prediction model. In addition, 75% split ratio and the five-fold cross-validation methods have been used to verify the prediction model. The multilayer perceptron algorithm has 76% accuracy value. The proposed prediction model is essential in directing R&D studies to the right technology areas and transferring the incentives to patent applications with a high quality rate