Euclidean Distance-based Approach Research Articles

Assessing the Academic Performance of Turkish Universities in 2023: A MEREC-WEDBA Hybrid Methodology Approach

Research and reporting on university rankings serve as valuable tools for students in evaluating universities and understanding their current performance status. Within academic literature, university rankings are established using diverse criteria across various domains, each carrying varying degrees of importance. This study adopts a multi-criteria decision-making (MCDM) perspective to analyze the academic performance ranking of Turkish Universities in 2023. Data sourced from the 2023 reports of sixty-one universities from Times Higher Education (THE) serve as the basis for this research, with THE indicators—teaching, research, citations, industry income, and international outlook—considered as primary research criteria. The Method based on the Removal Effects of Criteria (MEREC) method is employed to ascertain criterion weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) method is utilized for university ranking. The study identifies "citations" as the criterion of highest significance. Notably, the top-performing universities in the ranking include Çankaya University, Fırat University, and Bahçeşehir University. Furthermore, by comparing the rankings from this study with THE university rankings, the research offers tailored suggestions for universities. This study underscores the importance of deriving criterion weights from university performance datasets rather than relying on fixed weights, facilitating a more nuanced approach to university rankings. Moreover, it presents THE performance rankings for sixty-one Turkish universities, offering valuable insights for strategic planning within the university sector.

Comparative performance evaluation of multi-objective optimized desiccant wheels coated with MIL-100 (Fe) and silica gel composite

Metal-organic frameworks have been introduced as promising alternative materials to improve the performance of conventional desiccant wheels (DWs) in desiccant cooling systems. However, simultaneously achieving high dehumidification and energy performance of DWs coated with advanced desiccant materials is challenging owing to the trade-off between these indices. In this study, based on multi-objective optimization, the dehumidification and energy performance improvement of a DW coated with MIL-100 (Fe) (MCDW) over a conventional DW coated with silica gel (SGDW) and silica composite (SCDW) were evaluated. Based on the data measured in this study, a metamodel was developed using an artificial neural network to predict the performance of the MCDW. Furthermore, Morris sensitivity analysis was conducted to determine the decision variables. The optimal solutions were determined using multi-objective optimization and a Euclidean distance-based approach. Finally, a comparative performance evaluation between the optimized MCDW and SCDW was conducted under baseline conditions. As a result, the optimized MCDW showed higher dehumidification and energy performance than the optimized SCDW and SGDW.

Energy Efficient Access Point Placement for Distributed Massive MIMO

Distributed massive multiple-input multiple-output (D-mMIMO) is one of the key candidate technologies for future wireless networks. A D-mMIMO system has multiple, geographically distributed, access points (APs) jointly serving its users. First of all, this paper reports on where to position these APs to minimize the overall transmit power in actual deployments. As a second contribution, we show that it is essential to take into account both the radiation pattern of the antenna array and the environment information when optimizing AP placement. Neglecting the radiation pattern and environment information, as generally assumed in the literature, can lead to a power penalty in the order of 15 dB and 20 dB, respectively. These results have been obtained by formulating the AP placement optimization problem as a combinatorial optimization problem, which can be solved with different approaches where different channel models are applied. The proposed graph-based channel model drastically lowers the computational time with respect to using an ray-tracing simulator (RTS) for channel evaluation. The performance of the graph-based approach is validated via the RTS, showing that it achieves 5 dB power saving on average compared with a Euclidean distance-based approach, which is the most commonly used approach in the literature.

Evaluation of the suitability of Fe40Co30Ni30 as a precursor for Fe-rich FeCoNi-based high-entropy semi-hard magnets

We evaluated the suitability of Fe40Co30Ni30 as a precursor for Fe-rich FeCoNi-based high-entropy semi-hard magnets. Mechanically alloying the metal powders 40 at.% Fe, 30 at.% Co, and 30 at.% Ni for 12 h effected micron-size Fe40Co30Ni30 powder comprised of nanocrystalline γ phase. Fe40Co30Ni30 evinced semi-hard magnetic properties at room-temperature—saturation magnetization (MS) ~146 Am2/kg and intrinsic coercivity (HCI) ~4.30 kA/m—as well as at below-room-temperatures. The MS at 0 K, μH, and HCI at 0 K were estimated as ~155 Am2/kg, ~1.60 μB, and ~7 kA/m, respectively. Alloy powders after thermal-treatment exhibited tunable semi-hard magnetic behavior at below-room temperatures and room temperature; their magnetic properties were comparable to certain commercial semi-hard magnets. The weighted Euclidean distance-based approach of decision-making proposed the Fe-rich FeCoNi medium-entropy alloy—Fe40Co30Ni30—as a favorable precursor, having an optimal combination of properties, for designing and fabricating novel FeCoNi-based high-entropy semi-hard magnets.

Extraction of Semantic Content and Styles in Comic Books

Digitisation of comic books would play a crucial role in identifying new areas in which digital comics can be used. Currently, existing systems in this domain lack the capacity to achieve complete digitisation. Digitisation requires a thorough analysis of the semantic content within comic books. This can be further sub-categorised as detection and identification of comic book characters, extraction and analysis of panels as well as texts, derivation of associations between characters and speech balloons, and analysis of different styles of reading. This paper provides an overview of using several object-detection models to detect semantic content in comics. This analysis showed that, under the constraint of limited computational capacity, YOLOv3 was the best-suited model out of the models evaluated. A study of text extraction and recognition using Optical Character Recognition, a method for determining associable speech balloons, as well as a distance-based approach for associations between characters and speech balloons are also presented here. This association method provides an increased accuracy compared to the Euclidean distance-based approach. Finally, a study on comic style is provided along with a learning model with an accuracy of 0.89 to analyse the reading order of comics.

Prediction of Farnesoid X Receptor Disruptors with Machine Learning Methods.

The farnesoid X receptor (FXR) emerges as a promising drug target involved in regulating various metabolic pathways, yet some xenobiotic compounds binding to FXR would be an important determinant to induce the receptor dysfunctions that lead to undesirable side effects. Thus, it is critical to identify potential xenobiotics that disrupt normal FXR functions. In this work, five machine learning methods coupled with eight molecular fingerprints and 20 molecular descriptors were used to develop classification models for prediction of FXR binders. The built models were evaluated using the test set and two external validation sets. The best model was obtained using a combination of molecular descriptors and fingerprints, which exhibited the AUC values of 0.83 and 0.92 for the test set and the first external validation set, respectively. The overall prediction accuracy for the second external validation set with the best model was over 85%. Furthermore, several representative privileged substructures that are essential for FXR binders, such as benzimidazole, indole, and stilbene moiety, were detected using information gain and substructure frequency analysis. The applicability domain analysis via the Euclidean distance-based approach demonstrated a marked impact on the improvement of prediction accuracy. Overall, our built models could be helpful to rapidly identify potential chemicals binding to FXR.

Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor.

Toxic chemicals entered into human body would undergo a series of metabolism, transport and excretion, and the key roles played in there processes were metabolizing enzymes, which was regulated by the pregnane X receptor (PXR). However, some chemicals in environment could activate or antagonize human pregnane X receptor, thereby leading to a disturbance of normal physiological systems. In this study, based on a larger number of 2724 structurally diverse chemicals, we developed qualitative classification models by the k-nearest neighbor method. Moreover, the logarithm of 20 and 50% effective concentrations (log EC 20 and log EC 50) was used to establish quantitative structure-activity relationship (QSAR) models. With the classification model, two descriptors were enough to establish acceptable models, with the sensitivity, specificity, and accuracy being larger than 0.7, highlighting a high classification performance of the models. With two QSAR models, the statistics parameters with the correlation coefficient (R 2) of 0.702-0.749 and the cross-validation and external validation coefficient (Q 2) of 0.643-0.712, this indicated that the models complied with the criteria proposed in previous studies, i.e., R 2>0.6, Q 2>0.5. The small root mean square error (RMSE) of 0.254-0.414 and the good consistency between observed and predicted values proved satisfactory goodness of fit, robustness, and predictive ability of the developed QSAR models. Additionally, the applicability domains were characterized by the Euclidean distance-based approach and Williams plot, and results indicated that the current models had a wide applicability domain, which especially included a few classes of environmental contaminant, those that were not included in the previous models.

Human Intention Inference Using Expectation-Maximization Algorithm With Online Model Learning

An algorithm called adaptive-neural-intention estimator (ANIE) is presented to infer the intent of a human operator’s arm movements based on the observations from a 3-D camera sensor (Microsoft Kinect). Intentions are modeled as the goal locations of reaching motions in 3-D space. Human arm’s nonlinear motion dynamics are modeled using an unknown nonlinear function with intentions represented as parameters. The unknown model is learned by using a neural network. Based on the learned model, an approximate expectation-maximization algorithm is developed to infer human intentions. Furthermore, an identifier-based online model learning algorithm is developed to adapt to the variations in the arm motion dynamics, the motion trajectory, the goal locations, and the initial conditions of different human subjects. The results of experiments conducted on data obtained from different users performing a variety of reaching motions are presented. The ANIE algorithm is compared with an unsupervised Gaussian mixture model algorithm and an Euclidean distance-based approach by using Cornell’s CAD-120 data set and data collected in the Robotics and Controls Laboratoy at UConn. The ANIE algorithm is compared with the inverse LQR and ATCRF algorithms using a labeling task carried out on the CAD-120 data set.

Weighted Euclidean distance-based approach as a multiple attribute decision making method for manufacturing situations

The selection of appropriate alternative in the manufacturing environment is a very important but at the same time a complex and difficult problem because of the availability of wide range of alternatives and similarity among them. So, there is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations. In this paper, a new multi-attribute decision making (MADM) method, ‘weighted Euclidean distance-based approach (WEDBA)’ is developed and applied to various decision making situations of the manufacturing environment. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Four examples are presented to illustrate the potential of the proposed WEDBA method and the results are compared with the results obtained by using other decision making methods.

Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach

A flexible manufacturing system (FMS) is designed to combine the efficiency of a mass-production line and the flexibility of a job shop to produce a variety of products on a group of machines. Even though a number of studies were proposed in the past to address the issue of evaluation and selection of FMSs, there is a need for a simple and systematic method to guide user organisations in taking a proper decision. In this paper, a new multiple attribute decision making (MADM) method, named as ‘Euclidean distance-based approach (EDBA)’, is presented for the selection of an FMS from a set of mutually exclusive alternatives. The results are compared with the results obtained by using other decision making methods. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Three examples are presented to illustrate the potential of the proposed approach.

Space and habitat use of moose in southwestern Sweden

Increasingly, wildlife managers and land managers are challenged to maintain the viability of large mammal populations. Although the moose (Alces alces) is an ecologically, economically, and culturally important ungulate species found throughout most of Sweden, little is known about its ecology throughout the southern part of the country. We collected baseline ecological data on spatial and habitat use patterns in urbanized southwestern Sweden by fitting 22 adult moose (13 F, nine M) with global positioning system (GPS) radio collars. Home range size of cows did not differ among seasons (P > 0.10); however, bulls had larger home ranges during fall than all other seasons (P < 0.010). Mean home range size of males during fall and spring was larger than females during any season (P < 0.010). We used a Euclidean distance-based approach to analyze multi-scale habitat selection by moose. Moose of both sexes and during all seasons selected boreal forest and mires when establishing a home range (P < 0.10). Moose had seasonal differences in habitat selection within their home range (P 0.001), and generally selected clear-cuts and early successional forests, mature coniferous forests, and glades, but avoided agricultural areas and open water. Habitat and space use characteristics of moose in our urbanizing study area were similar to characteristics reported for moose in forest-dominated landscapes of Fennoscandia.

Space and Habitat Selection by Female European Wild Cats (Felis silvestris silvestris)

Studies on the use of space and habitat selection of threatened species are useful for identifying factors that influence fitness of individuals and population viability. However, there is a considerable lack of published information regarding these factors for the European wildcat ( Felis silvestris ). Serra da Malcata Nature Reserve (SMNR), a mountainous area in the eastern centre of Portugal, hosts a stable wildcat population which constitutes a priority in terms of conservation. We studied space use and habitat selection of female wildcats in SMNR with the following objectives: 1) to describe seasonal space use and habitat selection and 2) to obtain information on priority habitats for wildcats in order to develop a proper conservation strategy. We used radio-telemetry as the basic tool for our study and we analysed habitat selection using an Euclidean distance-based approach to investigate seasonal and annual habitat selection by wildcats. We detected that during spring females exhibit smaller home ranges and core areas. Females exhibited habitat selection for establishing home ranges from the available habitats within the study area. In fact, females selected Quercus pyrenaica forests and Quercus rotundifolia and Arbutus unedo forests positively and avoided Erica spp. and Cistus ladanifer scrubland and other habitats. Quercus pyrenaica forests and Quercus rotundifolia and Arbutus unedo forests are important habitats for female wildcats because they provide shelter and food resources, such as small mammals. They also contain elevated tree cavities which can be use as dens. In contrast, Erica spp. and Cistus ladanifer scrubland is an extremely dense habitat with low associated biodiversity and so wildcats avoid it. We believe that this habitat, as well as pine stands, do not provide food and cover resources for wildcats. Home ranges with higher percentage of these habitat types tend to be larger, since females are required to use larger areas to meet their resource requirements. Our results emphasize the importance of the remaining autochthonous forests in wildcat conservation. Therefore, we recommend that current habitat policy for restoration and conservation should be continued and expanded in order to substantially increase the amount of natural forested land in Serra da Malcata.