Polarization-sensitive OCT (PS-OCT) data were visualized by a differential Stokes-based processing and the derived local retardation. The left and right ears of a healthy volunteer were examined.Distinct retardation signals in the annulus region of the TM and near the umbo revealed the layered structure of the TM. Due to the TM’s conical shape and orientation in the ear canal, high incident angles onto the TM’s surface, and low thicknesses compared to the axial resolution limit of the system, other regions of the TM were more difficult to evaluate.in vivo. Further investigations on healthy as well as pathologically altered TMs are required to validate the diagnostic potential of this technique.The use of endoscopic PS-OCT is feasible to differentiate birefringent and nonbirefringent tissue of the human TM  While OCT has been successfully commercialized in ophthalmology for diagnostics of the eye, the application of both morphologic and functional OCT imaging for hearing diagnostics has attracted significant interest in recent years.–The eardrum or TM of conical shape separates the ear canal from the middle ear and receives incoming sound waves. It is connected to the malleus of the ossicular chain, whereby vibrations of the TM are conducted to the inner ear mechanically by the following ossicles incus and stapes. The main region of the membrane is the pars tensa surrounded by the annulus, a fibrocartilaginous ring, whereas the minor part, the pars flaccida, with only loose connective tissue is found superior to the malleus handle. The structure of the pars tensa is described to be composed of three layers:ex vivo OCT measurements by Van der Jeught et al.In vivo thicknesses observed with endoscopic OCT were provided recentlyWhile only limited insights onto the thickness of the lamina propria, especially in humans, are reported, the full thickness distribution of the human TM was evaluated from ,In general, the acoustical and mechanical properties of the TM are a critical part of the auditory system and depend largely on the scaffold of the lamina propria.Besides this broad foundation from anatomical and physiological studies, Mozaffari et al.,in vivo.Different optical technologies provide a connective tissue-specific contrast, e.g., due to the anisotropy and birefringence of collagen fibers, which is most frequently exploited by means of polarized light microscopy.in vivo imaging applications. While PS-OCT has been demonstrated for manifold applications including the retina,,,,,Polarization-sensitive OCT (PS-OCT), an extension of OCT that utilizes the contrast from polarization changes in tissue similarly to polarized light microscopy, has the potential to bridge the gap between those modalities and real-time in vivo and a Stokes-based processing approach to determine a local retardation contrast. By examining both ears of a healthy volunteer, we demonstrate the feasibility of the developed endoscopic PS-OCT system in assessing the connective tissue and thus, the layered microstructure of the TM as well as the limitations therein. Finally, we discuss the current limitations, both due to the anatomical constraints and due to the system properties, as well as strategies to address these for potential clinical applications and successful translation.Here, we describe the modifications of a previously developed endoscopic OCT system to perform PS-OCT measurements of the human TM 22.1The endoscopic PS-OCT system used in this investigation is based on the setup described by Kirsten et al.To adjust the polarization state of the incident and returning light through the single-mode fibers (SMF), polarization-controlling paddles were integrated in both arms. The amount of glass added by the GRIN optics caused a dispersion mismatch, although the chosen fiber length in the reference and sample arm compensated the effect to some extent. The remaining dispersion was software compensated during postprocessing.The most important modification, compared to the system used beforehand, is the replacement of the detection unit to implement a polarization-sensitive system. Therefore, the back-reflected sample and reference light were guided by fiber collimators to the free-space polarization-diverse balanced detection unit. The polarization of the reference arm was controlled to be diagonally polarized by a polarizer placed at 45 deg in the optical path. In the following, the light beams of the two interferometer arms were superimposed at a 50:50 nonpolarizing beam splitter. The outgoing light was then separated in orthogonally polarized parts by two adjacent polarizing beam splitters. The optical outputs were detected separately for each polarization channel by two identical balanced photoreceivers . The resulting differential signals were low-pass filtered and acquired by a high-speed digitizer , which used both the provided 2.2The study followed the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board (IRB00001473) at the TU Dresden (EK 252062017). TM examinations utilizing the endoscopic PS-OCT system were conducted by an experienced otolaryngologist in the clinical setting similar to generally performed otoscopy. Volumetric scans were acquired of the left and right ear of a healthy volunteer . The subject sat on a common examination chair adjustable in height, the examiner on a doctor’s chair with an elbow rest for stabilization. The endoscope was inserted in the ear canal while the positioning was visually controlled by the provided camera image and a preview of orthogonal OCT cross sections. For volumetric scanning, the proximal surface of the GRIN optics was scanned with After Fourier transform processing, the axial range of 8 mm corresponds to 1024 samples and provides an axial resolution of around 2.3The measured balanced detection signals were processed following the conventional procedure for swept-source OCT data . The Fourier transform provided complex-valued, depth-resolved A-scans From this, the logarithmic intensity can be directly calculated as To evaluate the polarization information, the four Stokes parameters Accordingly, the reciprocal of the resulting first component provided the degree of polarization (DOP)By calculating the DOP in this order, i.e., first creating an incoherent ensemble by spatial averaging from which the depolarization effect of the sample can be derived,,,Now, each three-component set of Stokes parameters 2.4As the polarization state of light represented by the Stokes parameters in entation , the Stoen face image (c). The curvature of the measured signal illustrates the distortion caused by the fan-shaped scanning of the plane microscope slide (a). Although this distortion can be corrected by an appropriate geometrical transformation as previously describedAs sketched in the simulated beam path of Furthermore, the polarization states were evaluated  that arein vivo measurement of a right TM  Bes3in vivo examination of a right human TM that is also visualized in –in vivo is assumed,Besides the described depolarizing effects, in the area of steep slopes and thus unfavorable beam angles of the light onto the TM’s surface, the retardation signal vanished. While this signal decrease comes in concurrence with the lower back-scattering intensity signal due to the incident angle, also the spatial alignment of beam incidence and 3D optic axis orientation are detrimental. As the polarization-sensitive measurement is always a projection of the 3D vectorial birefringence onto the plane perpendicular to the incident beam, the effective retardation approaches zero when optic axis and incidence converge. In consequence, the measurements of steep regions are especially difficult to evaluate.Further evaluation showed the presence of high retardation signals in the left ear volume consistently as demonstrated in While 4in vivo endoscopic OCT,Compared with the results of GRIN-based Furthermore, the modification of the revised system to be a polarization-sensitive instrument was also advantageous for the intensity-based imaging by itself. Evaluating the total intensity as sum of the intensities detected from both orthogonal polarization channels while controlling a diagonally linear polarization in the reference arm prevented polarization-dependent extinction of the interferometric signal, which can otherwise result from the mismatch between the polarization states of the interferometer arms.in vivo. Significant retardation signals in the annulus region and close to the umbo are in good agreement with the available anatomical studies about the layered structure of the TM and in particular collagen fibers in the lamina propria,The investigation shows that the usage of endoscopic PS-OCT enables identifying birefringent tissue of the human TM ex vivo PS-OCT measurements,,Nevertheless, based on previous light and electron microscopic surveys,In general, the examination of the TM is relatively constrained due to the shape and limited flexibility of the ear canal. The conical form of the TM and its tilted position at the end of the ear canal imply that a significant part of surface area is examined under a steep angle, even though fan-shaped scanning was used. While the marginal areas of the TM and the anterior region around the umbo provide the best back-scattering signal due to an almost perpendicular illumination, the incoming beam approaches a nearly parallel incidence with respect to the TM’s surface in the posterior half. For such large angles, even larger than 70 deg, the polarization of the light penetrating the tissue is changed as described by the transmission coefficients of the Fresnel equations, which is effectively diattenuation. Although this contribution was not considered in the applied processing, using multiple input states would facilitate to determine the diattenuating behavior.ex vivo PS-OCT measurements seem to support the presumed increased thickness of the collagen layer,in vivo. Accordingly, there is a need for more comprehensive ex vivo measurements with both higher resolution and appropriate positioning of the temporal bone specimen to compensate for the regularly tilted TM in the ear canal. Although increasing the lateral resolution is essentially limited by the geometry of the ear canal and a necessarily safe working distance, merely enhancing the axial resolving power should be highly beneficial in vivo. As the reflexes occurring at the surfaces, i.e., the border between air and tissue and vice versa, generate dominant signals that overlap with the less intensely scattering inner layer, axially separating those contributions promises considerable improvements.In addition, the thickness of the lamina propria tends to correlate with the overall membrane thickness, as the regions of high retardation correspond to the thickest regions of the TM as stated by Van der Jeught et al.Additionally, the current axial resolution limit of the system and processing of ,,Similarly, a more sophisticated polarization processing can be more exhaustive than the simplified approach shown here. While solutions such as the mirror-state approach,,in vivo investigations of both healthy and pathologically altered TMs.Endoscopic PS-OCT has the potential to serve as a diagnostic tool in middle ear cases that benefit from the ability to discriminate tissues or structures more specifically than it is feasible through the intensity signal. Even without pathological alterations, e.g., for intraoperative imaging and surgery guidance when placing a prosthesis optimally, a better recognition of structures is beneficial. Among the assessment of pathological changes, the diagnosis of tympanosclerosis is a promising application as it causes the calcification of connective tissue, which might be detected by characteristic polarization signals, i.e., reduced birefringence or depolarization. Presumably, the demarcation of, for example, sclerotic tissue from the ossicular bones or for better evaluating the pathological condition based on the degradation of the fiber structure can be visualized. Similarly, those advantages apply for the evaluation of tympanoplasty outcome.Overall, this sum of application opportunities, even though not crucial, combined with the minor complexity of redesigning the OCT system for being polarization sensitive as shown here and accordingly the manageable effort for clinical translation, should be considered when thinking about introducing OCT in otolaryngology.5in vivo. While its layered structure and approximate distribution of connective tissue are described in the available literature, which is mainly based on ex vivo investigations, both clinical and preclinical applications would benefit from an imaging method capable of directly examining the TM with tissue-specific contrast in vivo.Current diagnostic and imaging methods lack the ability to assess the microstructure of the human TM in vivo is investigated. While regions of significant retardation signals were in agreement with the anticipated occurrence of a thicker fibrous layer, i.e., near the annulus ring and in the surroundings of the umbo, it was not possible to track collagen-related signals across the entire TM. In general, the geometry of the TM within the ear canal poses a challenge by almost parallel surfaces with respect to the incident beam in certain regions. Therefore, future improvements are primarily expected in the determination of the local retardation contrast and the birefringence evaluation. As the described system extension toward PS-OCT was comparably effortless, enhancing a proposed OCT system with polarization-diverse detection might be considered not only if the additional information is specifically required but also for fundamental insights. In the case of middle ear imaging, further in vivo studies are required to gain an extended knowledge about the polarization dependency of pathological changes as well as healthy tissue, which will eventually lead to a comprehensive review of the clinical potential.In this study, an endoscopic PS-OCT was developed and its capability of differentiating the birefringent from nonbirefringent tissue in the human TM Click here for additional data file.


Remote areas, such as the Amazon Forest, face unique geographical challenges of transportation-based access to health services. As transportation to healthcare in most of the Amazon Forest is only possible by rivers routes, any travel time and travel distance estimation is limited by the lack of data sources containing rivers as potential transportation routes. Therefore, we developed an approach to convert the geographical representation of roads and rivers in the Amazon into a combined, interoperable, and reusable dataset. To build the dataset, we processed and combined data from three data sources: OpenStreetMap, HydroSHEDS, and GloRiC. The resulting dataset can consider distance metrics using the combination of streets and rivers as a transportation route network for the Amazon Forest. The created dataset followed the guidelines and attributes defined by OpenStreetMap to leverage its reusability and interoperability possibilities. This new data source can be used by policymakers, health authorities, and researchers to perform time-to-care analysis in the International Amazon region. In the study of neglected tropical diseases, GIS analysis is primarily used to study epidemiology and surveillance3. GIS studies involving access to care have shown that rural and remote areas remain a global concern for policymakers4. Topographical characteristics of remote areas, such as the Amazon Forest, create challenges to adequate access to healthcare. Additionally, addressing these challenges using data-driven solutions is difficult due to the scarcity of open-source digital geographic representations data of these locations, specifically for transportation route networks. This data gap limits the development of reliable analysis for these remote and rural regions.Geographic Information Systems (GIS) analysis is a powerful tool for evaluating the quality of access to medical services and acquiring fundamental data for medical decision-making5. For example, OpenStreetMap (OSM), an open-source, volunteer geographic information mapping project, seeks to create and provide accessible worldwide geographic data on transportation route networks6. In the past decade, volunteers, corporations, governmental, and humanitarian organizations have developed open-source spatial data, which contributed to and used the open geographic database of OSM for various purposes7.While most developed countries rely on expensive GIS instruments from governmental and ordinance surveys, developing countries depend on other alternatives to overcome this limitation8. Nevertheless, data from low-income countries is uncertain, and their studies based on OSM road-based transportation route network data can have unreliable information9. For example, some regions, such as the Amazon Forest, have limited data availability. This region, in particular, stands out for the absence of roads, lack of regular road-based transport, and significant geographic barriers10. The movement of people and goods in transit between the countryside and cities is highly dependent on fluvial transportation13. In this large low-resource region, it is crucial to include rivers in developing a geographical representation of transportation route networks to better model real-world movement of people. The current inability of using rivers as a platform to perform distance analysis in the Amazon hinders appropriate data generation for research, effective disease intervention implementation, and policy-making. Any distance analysis performed in the Amazon region not taking into consideration the rivers as a pathway of transportation is doomed to be inaccurate and erroneous. Despite its importance for characterizing the Amazon Forest, to our knowledge, there is no study dedicated to developing transportation route network data sources that includes rivers as the means of people’s transportation.It is estimated that OSM provides an 83% complete road-based transportation route network, with more than 40% of countries having a fully mapped road network15. Without good methods to create a river connectivity network, the geographical representation of rivers as a means of transportation would not be possible. Our proposed database uses elements of river connectivity, but our goal was to use the geographical elements that describe river connectivity and convert them into a database structure that would be linkable to existing road-transportation networks and create vehicle-accessible paths that combined roads and rivers in the Amazon Forest. Therefore, our dataset represents how rivers can be used to create routes reflecting travel time or distance linking two points within a transportation network16.The river routing literature provides methods and approaches to define hydrological connectivity, represented by the river’s connectivity network, flow direction, and surrounding terrain18.Our objective was to develop a database that adapts the existing digital representation of the Amazon Forest rivers into a river-based transportation route network. To achieve this objective, we combined the river’s spatial representation with existing parameters of road-based transportation networks to allow the creation of multi-modal transportation pathways. In our database development study, we leverage previous work dedicated to studying river routing from the hydrology perspective. We combined the OSM road-based transportation network with previous existing rivers representation data freely available: the Hydrological Data and Maps Based on Shuttle Elevation Derivatives at Multiple Scales (HydroSHEDS) and the Global Rivers Classification (GloRiC)In this database development study, we combined three different data sources to develop our river- and road- transportation route network dataset applied to the Amazon Forest. Our first data source was the OSM database, which served as the primary data model for our development framework as our transportation routing notation. The OSM is the largest available source of representation of road-based transport networks. By applying the same notation to our database, we are expanding the possible uses of our dataset. The regular transportation route approaches widely applied to OSM could be replicated for this new dataset while prioritizing interoperability.17. In response to these limitations, a team of scientists have developed the HydroSHEDS, a database and mapping representation of the world’s rivers that provide the research community with reliable information about rivers locations on the Earth’s surface and how water drains the landscape19. To create a rivers-based transportation route network dataset for the Amazon Forest rivers as the basis to perform travel time and distance analysis, we adapted the Hydrological Data and Maps Base of HydroSHEDS21. The processing steps of generating HydroSHEDS are detailed in the data set’s technical documentation22.Our second data source was the HydroSHEDS database. This served as the basis for our river’s geographical representation. While countless hydrographic maps exist for well-known river basins and individual nations, there is a lack of seamless, high-quality data on large scales such as continents or the entire globe23. We tested the National Waters Agency (ANA) from Brazil; however, the database was composed of multiscale mapping data that provided heterogeneous resolutions of the hydrology in the area25. These resolution changes impacted the density of river streams in the analytical space, affecting the standardization of the displacement estimation. This resulted in a bias in terms of distance or travel time to access. Thus, we opted to use the HydroSHEDS data source because the river distribution is homogenous across the entire region analyzed.We used HydroSHEDS because it provided the best information across the geographical space of the Amazon Forest. Although HydroSHEDS has known limitations (discussed in the HydroSHEDS v.2 development website), such as low-quality resolution on some areas of the globe, it proved to be the best solution for our problemIn addition, the HydroSHEDS dataset has several characteristics not present in the ANA database that are similar to the road-based transportation networks, facilitating interoperability. The possibility of having lines connected by nodes to other lines creates the network structure needed to perform transportation route estimation. A transportation route estimation can be understood as an effort to estimate the best route between two or more points spatially distributed in terms of distance or time. A transportation route network dataset comprises the geographical representation of line segments (river segments) interconnected (river connectivity). Beyond the spatial representation, each line in the dataset has secondary attributes such as length, travel time, maximum supported width, direction, and maximum speed. Based on the network notion of HydroSHEDS, we were able to develop an approach to convert the geographical representation of the rivers to a transportation route network dataset.In addition to the HydroSHEDS database, we incorporated variables from the GloRiC database, our third data source. GloRiC provides river types and sub-classifications for all river reaches contained in the HydroRIVERS database. From GloRiC, we included river characteristics such as flow regime and river stream speed. We chose GloRiC due to its compatibility with the geographical representation provided by the HydroSHEDs dataset.We prioritized the interoperability of our database with the community already using the OSM to perform routing analysis, facilitating the incorporation of our new dataset into existing data processing pipelines. We used the variables existing in the GeoFabrik Routable ShapeFiles from OSM. The option to use the GeoFabrik allows for additional quality checking performed over the raw version of the OSM files.We conducted a cross-reference and compatibility assessment of the HydroSHEDS hydrographic dataset to the OSM standard. Despite the spatial representation similar to a road-based transportation network, the HydroSHEDS hydrographic dataset bears no similarity in columns informing the river code, hydrological, physio-climatic, and geomorphic data. Thus, to be able to convert the hydrologic information from HydroSHEDs into a transportation route network dataset, we had to match the OSM data notation with the available information of river characteristics in the Amazon Forest region.The HydroSHEDS raw dataset provides data on the length of the river segment (in kilometers), but does not provide information on key features needed to calculate travel time to the closest healthcare facility. For each river segment, these features include: (a) the coordinates for the starting and ending point; (b) the estimated navigation speed for a standard boat/ship; (c) the segment access connectivity to the river network; (d) the estimated travel time through the river segment; (e) the river’s water stream flow speed; (f) seasonal variation on the flow regime; and (g) the river’s water discharge volume.27. Using these parameters, we were able to calculate the average travel time to navigate through a given river segment.To address this issue, we created additional fields in the database to characterize the Amazon Forest rivers using information available through literature review  can reach up to 37 km/h11. In our database, we created a variable to represent the average speed and the maximum speed allowed.The second step defined the creation of the transportation attributes for each river. The transportation attributes are used to estimate travel time over a segment. These attributes include: the maximum speed allowed over a river segment; the maximum width, weight, and height allowed for a boat/ship using a specific river segment; the description of whether a river segment is accessible on foot; the estimated speed (in kilometers per hour) for a normal motorized boat/ship on this river segment; the latitude and longitude of the start and endpoints of the river segment; and the others parameters defined in Supplementary Table Still on the second step, we added three additional variables that can be used to characterize the rivers from the GloRiC database. The variables are river stream speed, river flow regime variability, and river discharge level. The first variable is a factor that should be applied to the boat speed, as the maximum speed can change considering the navigation upstream or downstream. The riverboat velocity is the physical resultant of the boat plus or minus the river stream speed, depending on the stream navigation direction. The river flow regime can be used to identify rivers with large variability during the dry season that could affect navigability. Most parts of the Amazon rivers present a medium variability in terms of water level during the dry season. A total of 91% of the rivers in the dataset were categorized as having low (5%) or medium variability (86%) between seasons. The river discharge level variable can be used to filter rivers in terms of size, reflecting the rivers with larger chances of being navigable.14. We opted to use this labeling strategy to maximize the potential uses of the data generated.The other variables comprising type of surface and transportation class were set to follow the codebook associated with the OSM standard, as defined in the third analytical stepThe resulting dataset from the three analytical steps is tabular data containing all the attributes of a routable dataset. To combine these attributes with the geographical representation of the rivers, we performed a linkage between the tabular data and the shapefile with the geographical representation of the Amazon Forest rivers. Figure 28. The data source contains 897,846 river segments and 997,548 streets, roads, and highways covering the international Amazon Forest region. The river courses in Brazil, Bolivia, Peru, Ecuador, Colombia, Venezuela, Guyana, and French Guiana are covered in the new dataset, as represented in Fig. The shapefile containing the river-based transportation route network database is stored in a repository28. The main file is a shapefile including metadata regarding river characteristics in the International Amazon Region. The dataset is described in Supplementary Table Here we present the metadata of our database, stored at a public repository17No other dataset uses a Digital Elevation Model with a higher resolution and precision as the data provided by the SRTM. The NASA SRTM mission overcomes challenges in terms of data generation, surpassing limitations related to spatial resolution, data structure, multiscale approach, representation of hydrological connectivity through river networking routing, and integrated data and modeling framework14.Due to the use of the new data from the Shuttle Radar Topography Mission (SRTM), the HydroSHEDs database represents the best hydrological data available.To demonstrate the potential uses associated with the new dataset, we analyzed the catchment area of a healthcare facility. Catchment area is defined as the geographic coverage using our rivers- and road- transportation route network from a set travel time distance. This type of analysis creates a catchment area by navigating the transportation network originated at the point of interest, a healthcare facility, and uses this covered area as a reference to perform calculations of the population reached, availability of health services ratio per population, rates of diseases by location, and other types of supply-demand analysis.29. The resulting misplacement of healthcare infrastructure can result in crises, such as the oxygen scarcity in Manaus at the beginning of 2021, which led to several deaths from complications of COVID-1930. Thus, the correct measurement of distances, travel time to reach care, and available pathways between the population and a healthcare facility are essential to assuring adequate care, better health care policies, and adequate interventions aiming to improve the organization of the health system network.One application of catchment areas is to assess the population that would fall within reach of a specific health facility. Populations facing geographic barriers to access tend to present with worse health indicators in comparison to groups not facing access challenges. For example, the lack of information on where the eligible population is located represents a major issue to reach the target outcomes of health campaigns. Creating indices of access to health services often depends on data representing access routes. Inaccuracies in the access routes can lead to erroneous results in the identification of underserved areaset al. 202129. The resulting outcome of this analysis is a geographical polygon representing the maximum threshold of distance or travel time to reach a facility considering the transportation network available. Thus, the polygon represents the coverage area of a health facility up to the limit of distance or time, defined by the end-user evaluating access. To perform the comparative analysis, we used 622 community health centers (CHC) located in the Amazonas state, Brazil as a reference for health facilities. Each CHC is responsible for offering primary health care to the surrounding population.Three comparative approaches were used. In the first, we created the catchment areas close to community health centers in Amazonas state, Brazil using the road-based transportation route network dataset usually provided by GeoFabrik-OSM. The second part of the analysis was done using only the new rivers-based transportation route network database. The third analysis combined both datasets. All analyses followed the same methodology to create catchment areas using routable pathways as defined by Rocha In total, we created 517 catchment areas using only roads, ferry lines, and highways as paths of access to the CHC Fig. . The catOur results represent only the analysis of a part of the Brazilian Amazon region. We believe that the dataset presented can help several disciplines to assess more precisely time and distances in the entire Amazon region. The use of straight-line distance does not represent the actual challenges in terms of transportation in the Amazon region. A routable dataset using the rivers as pathways is an essential tool in the policy discussions regarding access to services in the Amazon region.Supplementary Table


The main aim of this study is to introduce an implicit personality assessment method  to Kuwait. We adapted an existing personality-related implicit association test , while also constructed the first trait EI IAT based on Petrides’ four-factor model. We investigated the psychometric properties of the implicit association test through assessing the reliability of scores and also their relationship with their corresponding explicit measures. The measures were administered to 1458 university students in Kuwait. The zero-order correlations showed that the explicit and implicit measurement approaches led to non-converging constructs in the case of both trait EI and the Big Five. Lastly, we believe that we were successfully able to introduce the concept of personality-related implicit association tests to the Kuwaiti sample. Subsequently, the IATs presented in our study will allow researchers to study a relatively new personality field, that is the implicit personality. Historically, psychologists interested in studying the personality differences among individuals relied on self-report measures. These measures are direct and scientifically categorised under explicit measures. Explicit measures assess mental structures and processes accessible through introspection . In otheIn contrast, when you do not ask people directly about their feeling but assess their behaviour or how they perform a task , you are using an indirect method. This indirect measurement method is referred to as implicit measure. Implicit measures are contrary to their explicit counterparts in which the earlier assess the mental structures and processes that are inaccessible through introspection . AccordiIn response to latency studies, researchers are interested in measuring the reaction times when participants perform a certain task. Thus, the participants are not asked about their feelings, instead, they focus on performing an objective task in which inferences are drawn from their timed performance.Implicit association tests, known as IAT , are thePetrides and Furnham  studied target and attribute.The IAT was introduced by Greenwald and colleagues  to assesThe IAT test comprises five separate categorisation tasks, represented by seven blocks. In the first categorisation task (Block 1), the participant is asked to sort words relating to the concepts  into categories. In the second task (Block 2), participants are asked to perform the same sorting task, but this time with different concepts . In the third task (Blocks 3 and 4), the categories are combined in a way that presents two concepts on the left of the screen, while the other two on the right . In the fourth task (Block 5), the placement of the concepts presented in Block 2 switches  and the participant is asked to perform the same sorting task. In the fifth task (Blocks 6 and 7), all concepts are combined again, but in a different combination than in Blocks 3 and 4 . Noteworthy, in the subsequent three IAT subtests, the first categorisation task is eliminated as it will already have been presented in the first subtest . An illustration of these blocks is presented in In concepts such as the Big Five and trait EI, several IATs, each called sub-IAT, are used to assess the underlying constructs. For instance, the Big Five IAT consists of five sub-IATs corresponding to the five constructs representing the Big Five . SimilarMe + Others) which is one of this IAT’s aims. Second, to avoid adding unnecessary trials that lead to longer tests, as adding these trials will result in including 140 more trials. Clearly, adding these trials will increase the likelihood of respondent fatigue [In both concepts, the first categorisation task was only presented in the first sub-IAT and eliminated from the following sub-IATs for two reasons. First, the participant has already been introduced to the concepts related to the first categorisation task  and others (by pressing the right assigned key “I” in English keyboard and “هـ” in Arabic keyboard). The same task is repeated in Block 2 with two different concepts, emotionality and logicality. In Block 3, the previous two tasks are combined, and the participants are asked to perform the classification task with two concepts on each side; when the stimulus belongs to the concepts me or emotionality, the participant will have to press the left key. The same task is performed in Block 4 but with more trials. In Block 5, the task in Block 2 is reversed. The participants will press the left key if the stimulus belongs to the logicality concept and the right key for the emotionality concept. In the last two blocks, Block 6 and 7, the concepts are reversed from Blocks 3 and 4, and participants are asked to perform the same classification task.As shown in me and others categories, in our study. However, in some cases, choosing the comparable category is not an obvious step. In such cases, it is advised to use a mutually exclusive category from the same domain. For example, Grumm and Collani [extraversion and introversion categories in their study. Therefore, we followed the advice to use an approach to choose the appropriate categories in our study.Several considerations must be concerned when constructing an IAT. The first consideration is related to defining the construct because this will affect the choices of the categories in the next stage. Many categories have an obvious comparison category, such as  Collani  used extstimuli matter when they discussed the contradictory attitudes toward America when the category America was presented by the names of certain presidents compared to flag images and other common sightseeing. Furthermore, they suggested avoiding negated stimuli because participants tend to take more time to process the negations and classify it properly, which indeed affects the response time. We believe that the stimuli must undergo pilot testing in which participants should be given the four categories and a shuffled list of all stimuli and asked to perform the classification task. By doing this, we can ensure whether the stimuli list under each category is appropriate.After choosing the appropriate categories, the IAT developer must ensure that the stimuli under each category are well-chosen. Lane et al.  stated sAs shown in Another stimuli-related concern is the number of stimuli used under each category. The first aspect involves whether an equal number of stimuli should be used under each category. This aspect is under-researched within the field; however, we see no clue in including the unequal number of stimuli under each category. The second aspect is related to the appropriate number of stimuli. Several studies using different numbers of stimuli  concluded that stimuli number does not affect the magnitude effect nor the reliability of the scores , 13.Furthermore, the IAT comprises three single-categorisation practice blocks  and four double categorisation critical blocks . We followed the suggestions in the literature  to incluLast, as suggested by Lane et al. , we inclIn IAT, the task response time is the measurement core, and all conclusions are based on it. As stated earlier, when the IAT was first presented, it was presumed that the participant would perform the classification task more accurately and faster if the associated categories shared the same keys on the keyboard.Me + Emotionality and Others + Logicality) are compared to the reversed set . This comparison estimates the association strength between the two sets of pairing. To elaborate, if the task response time is faster for the first set, we can conclude that the relative association in the first set is stronger than the other set. Hence, the participants reflect an implicit preference to view themselves as emotional over being logical.For that logic, the response time for each task is recorded and stored. Afterwards, the differences in the responses time to a certain pairing of the target and attribute  that overcomes all early IAT scoring methods issues. D is the difference in mean response time between the IAT’s two combined tasks , divided by its associated  standard deviation. They also recommended deleting all trials greater than 10,000 milliseconds  and all subjects for which more than 10% of their trials have a response time lesser than 300 milliseconds .At early stages, researchers reported the IAT effects as the differences in mean response time between two combined pairings  until Greenwald and colleagues  introducD. First, D minimises the correlation between the IAT effects and individuals’ mean response time. Second, it minimises the effect of the IAT blocks’ order. Third, it minimises the effect of IAT experience on selecting new IATs. Fourth, it retains higher internal consistency values. Last, it maximises the correlations between the corresponding implicit and explicit measures. Further, Rudman [D is used throughout this study during IAT use.Lane et al.  pointed , Rudman  pointed D score is called an IAT effect size. Rudman [D statistic estimates IAT effect size magnitude and the values of.15, .35, and .60 correspond to small, medium, and large effect sizes, respectively. Further, the D statistic can be transformed into a more familiar effect size measure, Cohen’s d, dividing D by the sample’s standard deviation.The IAT . Rudman  suggestepersonality is explained by the five-factor model.Undoubtedly, the five-factor model of personality, known as the Big Five, is the most popular personality model. Although the origin of this model is unclear, it is obvious that this model emerges based on Cattel’s system that depends on the factor analytic approach . The resNevertheless, the factor names and their interpretations were inconsistent among the researchers. For example, Norman’s  five facTrait emotional intelligence (trait EI) is described as a set of emotional perceptions that may be assessed using self-report measures . BecauseSeveral studies have been conducted to examine the relationship between the Big Five factors of personality and trait EI –29. ThisAlmost twenty years ago, the concept of implicit personality was introduced to the psychology field by Greenwald and Farnham . HoweverThe overall aim of this study is to introduce a novel approach to assess implicit personality through implicit association test (IAT) in Kuwaiti Arabic for use in general population. Specifically, we were interested in a) adapting the Big Five IAT and b) constructing the trait EI IAT. For the Big Five IAT, we adapted the English Big Five IAT developed by Back et al.  into KuwH1: There will be low correlations between trait EI scores obtained by the explicit (TEIQue-SF) and implicit (trait EI IAT) measures.H2: There will be low correlations between the Big Five scores obtained by the explicit (NEO-FFI) and implicit (Big Five IAT) measures.The ITC  test adaAfter piloting the Big Five IAT for the first time, the Agreeableness subtest showed an unacceptable reliability estimate of .42 through the split-half method. We contacted the committee to revise the stimuli, and the same translation procedure was followed again. In the forward translation stage, the committee members decided to use a different synonym of every problematic stimulus within this subtest. After that, the back-translation and the final revision procedures were followed. The committee approved the final version of this subtest and suggested performing a focus group before piloting the subtest again. The participants in the focus group were able correctly to categorise each stimulus under the purposed category without any mistakes. Therefore, we decided to proceed with the revised version of this subtest for the second pilot of the Agreeableness subtest within the Big Five IAT. Accordingly, the final version of the Big Five IAT comprised 5 sub-IATs corresponding to the five factors of: Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness.We also constructed the first trait EI IAT draft following the guidelines by Lane and colleagues . We creaAfter piloting the trait EI IAT, we looked at the reliability estimates obtained through split-half and Cronbach’s alpha methods. The estimates suggested a possible issue with the Emotionality factor of trait EI, as can be seen found in I consent to participate in this study”.We used a convenience sample design and approached participants via an anonymous Qualtrics link (online). These participants differed from those used for the pilot samples, and the final IAT versions were again implemented on the main study sample. Several faculty members within Kuwaiti higher education institutions were contacted individually to help disseminate the Qualtrics link. Participants did not provide any personal self-identifying information, and provided their consent to participate by clicking on “The Qualtrics link comprises five sections after consenting to participate in the study. The participants were asked to answer demographic questions in the first section. The second and third sections concerned the explicit and implicit Big Five factor measures, respectively. The fourth and fifth sections concerned the explicit and implicit trait EI measures, respectively.This study was approved by the University College London-Departmental Ethics Committee (CEHP/2021/586). All research methods were conducted in accordance with relevant guidelines and regulations.The first pilot sample of university students comprises 57 participants completed the trait EI IAT. The second pilot sample of university students comprises 64 participants completed the Big Five IAT. The third pilot sample of university students comprises 34 participants completed the revised Agreeableness sub-IAT. Sample sizes are complied with the suggestions from the measure’s piloting literature [The main study sample included 1458 university students in Kuwait with a mean age of 22.34 years (SD = 7.62 years). The characteristics of our sample can be found in Building IAT Blocks section.The TEI IAT is a four-IAT subtests reflecting the four factors model proposed by Petrides . The firIn general, for each block, a mutually exclusive stimulus that belongs to either a left or a right side of the concept will appear in the middle of the participant’s screen. The task asks the participant to classify each stimulus by pressing two pre-specified keys (a left and a right key) on his keyboard. During this classification task, the response time to each stimulus is recorded for further analysis. The list of stimulus and categories used in our study can be found in The Big Five IAT is a five-IAT subtests reflecting the big five personality dimensions proposed by Costa and McCrae . The Engwww.psychometriclab.com. In this study, we used the Kuwaiti-Arabic adaptation of TEIQue-SF [The TEIQue-SF is a 30-items inventory providing comprehensive coverage of the sampling domain of trait EI in adults . The iteEIQue-SF , which hThe NEO-FFI is the short form of the NEO-PI developed by Costa and McCrae . The invR 4.0.5 [iatgen package [button mashers and dropped from the final analysis if they had too many fast responses .Descriptive statistics and reliability estimates will be calculated using R 4.0.5 . The iat package  will be The reliability estimates will be computed based on creating pairs of reaction times from compatible/incompatible blocks, calculating their differences, and apply them to Cronbach’s alpha analysis .Additionally, we will model the interrelationships between the two implicit constructs  through SEM based on the relationships reported in Petrides et al. . The anaThree pilot studies were conducted to look at the descriptive statistics and reliability estimates of the scores obtained by the trait EI and the Big Five IATs. The test-related information of these pilot studies can be found in The second pilot study comprised 64 participants who completed the Big Five IAT. The results can be found in The third pilot study comprised 34 participants who only completed the revised Agreeableness factor of the Big Five sub-IAT and Emotionality factor of trait EI sub-IAT. The results can be found in The IAT-related information for the 9 sub-IATs are shown in Gender-based reliability estimates for all measures are shown in The zero-order correlations between the explicit-implicit constructs are shown in In this study we aimed to introduce the implicit personality concept to the Kuwaiti psychology field accompanied by adapting and constructing their measurement method. In the introduction, we distinguished between the concepts of explicit and implicit personality through their measurement methods. For instance, explicit personality is assessed through self-report measures by asking the participants to directly choosing a response from a given scale. While implicit personality is assessed indirectly using certain type of tests like the IAT.Accordingly, we adapted one IAT to assess the Big Five factors indirectly  in Kuwaiti sample. In more details, we adapted the five sub-IATs corresponding to the five-factor model of personality proposed by Costa and McCrae . The stiFirst, we assessed the psychometric properties of our IAT measures. Our reliability analysis yielded satisfactory estimates; however, we believe that the very meaning of internal consistency is questionable within the IAT context. This is because these estimates within the IAT context refer to whether responses time between trials are consistent or not, rather than whether the actual D-scores are consistent .Our results showed that, in general, reliability estimates for implicit measures were higher than for explicit measures. This is at odds with other research showing that explicit measures of personality tend to have higher reliabilities than implicit measures , 41, 42.We examined the relationships between the explicit and implicit measures of trait EI and the Big Five, separately, through zero-order correlations. The results supported our earlier hypotheses that we will find weak correlations between the scores obtained by explicit and implicit measures for both the Big Five factors and the four trait EI factors. These results are consistent with Lane et al.  who repoGiven the fact that this type of measure is introduced for the first time to Kuwaiti sample, we expected that the error rate will be relatively high in the first sub-IAT of each construct as shown by our results. However, the error rate decreased continuously as the participants approached the end of the IAT. This suggests that experiencing more IATs can help the participant to perform the categorisation tasks more accurately.The implications of the current study are manifold. First, concerning the field of personality, this study contributes to the broad personality literature in two unique ways. The first contribution is our attempt to construct the first trait EI IAT based on Petrides’s  four-facSecond, concerning policymakers, we believe that we have introduced a novel implicit personality measurement method, which can help us to identify personality traits that people are unwilling to report through self-report measures . This will benefit policymakers in Kuwait as well as the international context because several organisations rely on psychological measures and other job-related qualifications to hire certain staff members . In fact, this is not surprising as certain personality traits  showed significant predictive effects on job performance . AccordiWhile our study findings are robust, it is important to acknowledge the limitations of the study in order to provide a balanced and accurate interpretation of the results. In this study, we relied on a non-probability sampling method . Although it was the most appropriate for our study design because we wanted to recruit as large as possible a sample size with restricted time frame and funds . Yet, thAnother significant limitation in our study is the length of the included measures. Ben-Nun  suggesteWe presented the number of participants who completed each sub-IATs along with the number of button mashers from pilot and main studies. The results from all studies showed that the number of participants was decreasing as they approach the end of the IAT. Also, the number of button mashers was increasing as the participants approached the end of the IAT. Thus, suggesting that the respondent fatigue has occurred in these participants, even though we decreased the number of tasks to mitigate this phenomenon. As a further step, we eliminated these participants from our data analyses in order to avoid any threats to the validity of our results.Besides the aforementioned attempts to control the respondent fatigue, we also ensured that the instructions given to the participants were clear and concise. This is also to ensure that participants do not spend much of the study time in reading the instructions.In this study, we provided comprehensive tools to assess implicit personality. The measures presented here were adapted and validated following the most comprehensive and updated cross-cultural adaptation guidelines. This, in fact, is also a big contribution to Kuwaiti psychology, as it promotes the research of these novel constructs across different settings in the country.Two more important directions can also be pinpointed from the results of the current study. As we discussed earlier, one is the need to rethink about the concept of reliability  of implicit data. That said, future researchers should revisit the meaning of the reliability in this context. Also, they should consider coming up with a better methodology to assess and interpret the internal consistency of the scores. Another direction is the study of the meaning of the implicit aspects of the Big Five and trait EI. Our results clearly indicated no correlation between explicit and implicit measures, which raises the question of what implicit personality reflects. We believe that they refer to the unconscious and automatic aspects of a person’s personality, which are deeply ingrained in an individual and unconsciously influence their behaviour and thought patterns. Frankly, we believe that these two directions can be thought of as research projects in their own night.Taking altogether, we believe that this study makes a contribution by introducing implicit association test as a vehicle to assess implicit personality constructs in Kuwait. These implicit personality assessment methodologies are highly novel and original in Kuwaiti Psychology field. Not only in Kuwait, but the findings presented in the present study also contributes to the growing implicit personality literature, as we presented results from a relatively large sample. These results supported the idea that the implicit aspect of personality should be thought of as a distinct aspect of personality. Thus, researchers within the field should view it as a different aspect of human personality rather than only viewing it as another method to measure personality.S1 Table(DOCX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.


In multi-objective optimization, as long as only three or four conflicting viewpoints are present, an optimal solution can be determined by finding the Pareto front. When the number of the objectives increases, the multi-objective problem evolves into a many-objective optimization task, where the Pareto front becomes oversaturated. The key idea is that NMF aggregates the objectives so that the Pareto front can be applied, while the Sum of Ranking Differences (SRD) method selects the objectives that have a detrimental effect on the aggregation, and validates the findings. The applicability of the method is illustrated by the ranking of 1176 universities based on 46 variables of the CWTS Leiden Ranking 2020 database. The performance of NMF is compared to principal component analysis (PCA) and sparse non-negative matrix factorization-based solutions. The results illustrate that PCA incorporates negatively correlated objectives into the same principal component. On the contrary, NMF only allows non-negative correlations, which enable the proper use of the Pareto front. With the combination of NMF and SRD, a non-biased ranking of the universities based on 46 criteria is established, where Harvard, Rockefeller and Stanford Universities are determined as the first three. To evaluate the ranking capabilities of the methods, measures based on Relative Entropy (RE) and Hypervolume (HV) are proposed. The results confirm that the sparse NMF method provides the most informative ranking. The results highlight that academic excellence can be improved by decreasing the proportion of unknown open-access publications and short distance collaborations. The proportion of gender indicators barely correlate with scientific impact. More authors, long-distance collaborations, publications that have more scientific impact and citations on average highly influence the university ranking in a positive direction.Non-negative matrix factorization (NMF) efficiently reduces high dimensionality for  This quote inherently exposes the core problem of the present research: the higher the number of objectives (dimensions) is, the more solutions become optimal from a perspective. The selection of the optimal solutions based on three or more objectives is called multi-objective optimization.The present work is motivated by Einstein’s quote, “Everybody is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid.”, many-objective optimizations problem (MaOO). The main attribute of the problem is the curse of dimensionality.In most multi-objective optimization problems, the objectives are often conflicting. Finding the Pareto front can be the key to solving multi-objective optimization problems consisting of two or three objectives as it selects the optimal solutions. Yet, most Pareto-based methods cannot optimize a problem with more than four conflicting objectives . The ParA possible solution is to use dimensionality reduction to reduce the number of objectives and enable the successful application of Pareto fronts. The principal component analysis (PCA)  is a popOur research objectives are to develop a general algorithm that is capable of:Selecting the best solutions considering as many objectives as possible, and uncovering why they excel.curse of dimensionality, as high number of objectives result in more solutions becoming part of the set of optimal solutions, the Pareto front.Diminishing the Projecting the data to positive objective-space so that the reduced components do not contain negatively correlating objectives. This way, the applicability of the Pareto front remains.Assigning weights to a group of similar objectives to overcome subjectivity, while also reducing the impact of reduntant objectives on the objective-space.Consistently aggregate and select the most important objectives, improving the correctness and objectivity of the analysis, as the redundant variables may influence the components composition, and alter the weighting.The main idea is to meet the above-mentioned requirements by applying sparse non-negative matrix factorization (sparse NMF) , 4. The i.e., maximum, mean, median, minimum of the data. In the present work, the SRD method is employed for validation purposes.The conflicting objectives (or those ranked in reverse) need to be distinguished to understand the underlying structure of the objectives and the ranking outcome. The validation of the dimensionality reduction should ensure that the correlational analysis yields the same as other established methods. The sum of ranking differences (SRD) technique –9 perforThe goal of applying NMF with SRD filtering is to explore the relationships behind the factors and create aggregated variables from which the Pareto front can be formed. NMF provides positively correlating objectives, as opposed to the PCA, therefore, yields a valid Pareto front. The optimal solutions are ranked to determine university ranking based on the purely bibliographic CWTS Leiden Ranking 2020  dataset.The novelty of the application of NMF is the following:A dataset with many objectives facilitates the oversaturation of non-dominated solutions (the best ones). Therefore, we apply dimensionality reduction so that we can use the Pareto front to select from among the solutions in lower-rank approximation of the objective space. Similarly to the concept of the PCA-PROMETHEE method, we introduce NMF and the sum of ranking differences to validate the results, and overcome the saturation of the front. In contrast, we also include PCA, though, valid Pareto front cannot be placed on the PCA-based reduced solutions as conflicting objectives are aggregated into the same principal component. sparse NMF is added to the analysis to find the dominant objectives and remove the redundant and irrelevant ones to make the analysis more comprehensive.The objectives used for ranking the universities are contradictory, conflicting, and their evaluation is not trivial, though dimensionality reduction solves the problem. Even if the PCA provides some insights, the crucial grouping can only be completed by SRD and NMF (or their coupling).many-objective optimization problems are reverted to multi-objective optimization problems. The solutions in the reduced objective space are capable of hosting valid Pareto fronts, as the curse of dimensionality is diminished.We present the efficiency of the NMF for the dimensionality reduction of ranking problems, where We use NMF to enforce a non-negativity constraint on the data, therefore, incompatible objectives (with each other) cannot be grouped . We cover the low-dimensional projection of the solutions with a Pareto front due to the solely positively aligned objectives.We validate the NMF with the SRD method, both yielded a similar outcome (the order of the objectives).We employ sparse NMF to remove the redundant objectives, which results in a more balanced weighting, and show, which objectives are imperative to the analysis.We use NMF to cluster the indicators and universities. The method detects the cultural differences between nationalities by concentrating the aggregated objective on a component, and cultures may earn different aggregated scores. The reduced dimensionality incorporates almost all aspects of the original objective space and highlights these differences in the rankings.A complex MaOO problem is the ranking of universities, where various viewpoints need to be considered for a fair ranking. The CWTS Leiden Ranking  is a popmany-objective university ranking as thoroughly as this was and still is unknown to the authors.MaOOs are becoming influential to the world of optimization; by using NMF, a fast and efficient solution is proposed. The NMF is a standard method for implementing decision-making algorithms, and is often used in feature extraction, which can be considered as a MaOO problem . Yet, itZenodo.The sparse NMF is provided by the Non-Negative Matrix Factorization Toolbox . All resIn the following sections, the related works are discussed. Then the methodology is introduced, where the problem of the classical approach is presented. Next, the dimensionality reduction techniques, performance metrics, and the SRD method are described. Lastly, the outcome is discussed and concluding remarks are provided. Supporting information on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)  S1 Appencurse of dimensionality is the application of multi-criteria decision analysis (MCDA). Most approaches focus on evaluating the objectives through a set of feasible solutions. Many employ aggregation or feature selection. Aggregation evaluates objectives by an aggregation function such as mean or sum, while the feature selection selects the dominant or the most relevant objectives and ranks accordingly. An example commonly employed in MCDA is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) , i = 1, …, NMulti-objective optimization problems can be interpreted as the minimization of a set of objective functions where each zi≺Pzj) another, if it is not worse than the other in any objectives, and is strictly better in at least one objective.k denotes the running index of the objectives, zi and zj are considered as the realizations of the variable z, while i and j denote the (indices of) solutions so that i = 1, …, N;j = 1, …N;i ≠ j.The classical approach to solve the optimization problem is to find the set of Pareto optimal solutions. One solution dominates  (zi≺Pzjfk(z), k = 1…n objectives can conflict with each other, or can be redundant so the optimization problem becomes more difficult to solve. If the optimization consists of only a handful (n < 4) of objectives, then it is called multi-objective optimization. Including any additional objectives yields a many-objective optimization (MaOO) problem that retains the curse of dimensionality.The Pareto fronts can take up convex and concave forms or the mixtures of both. The border proposes different trade-offs. A concave Pareto front consists of strong trade-offs, while a convex front has relaxed ones . There cWhen the number of objectives increase drastically, the difficulties of MaOO problems  are enlathe deterioration of the performance of Pareto dominance-based search algorithms,the escalation in the number of solutions required to estimate the entire Pareto front,lack of visualization.curse of dimensionality. If the problem concerns dealing with several hundred objectives, the ranks are subjected to inflation, as more and more solutions obtain the same rank and hundreds will be firsts, seconds, etc. As a result, the number of non-dominated solutions expands exponentially, which causes the first two difficulties. The reason for the lack of visualization is the high dimensionality. One way to measure the curse of dimensionality is to apply Relative Entropy to the rankings of the objectives. As the number of objectives increases, the set of optimal solutions expands with additional ones  is lower dimensional projection of the realization of solution xi by function g to p dimensions. The reduced solutions are complied into a matrix denoted by WN×p. The reconstruction error measures the quality of the lower-dimensional approximation as the reduced set of objectives must retain the information content of the original set as much as possible. Thus our goal is to minimize the reconstruction error:wi low-rank approximation.Our main focus is to reduce the number of objectives from Most dimensionality reduction methods are built to reconstruct the original matrix from the projected space, aiming to represent the original matrix as accurately as possible. Therefore, the optimization problem seeks to minimize the difference between the original and reconstructed matrix.In the present study, three dimensionality reduction techniques are applied, namely PCA, non-negative matrix factorization (NMF), and sparse NMF. The essential difference between them is depicted in n to p, the MaOO problem devolves into a multi-objective optimization problem that can be solved by determining the Pareto front as dimensionality reduction decreases the effect of the curse of dimensionality sufficiently enough to be eligible for multi-objective optimization.By reducing the number of objectives Principal component analysis (PCA)  is a claW is often referred to as the score matrix, and is the projected data, while H describes the loadings (coefficients) of the matrix. As the requirement is that the product of the score, and the correlation matrices with themselves is to be one (I denotes identity matrix), both positive and negative numbers are allowed. The constraints ensures orthogonality, such that the aggregated objectives are independent of each other.In terms of the PCA, curse of dimensionality are no longer a problem. However, the PCs incorporate negatively correlating objectives. The solutions of the Pareto front are both optimized in the negatively and positively correlating objectives (ranking those first who are the best in both a negative and positive objective). In contrast, the preferable outcome of a dimension reduction method would be only to optimize the positively correlating objectives. The novelty of this work is that non-negative matrix factorization is applied as its lower-dimensional representations are only composed of positively correlating objectives whereas PCA may provide negatively correlation objectives in the principal components.PCA reduced the number of objectives so that the effects of the XN×n is decomposed into X ≈ WH, where W denotes an N × p (score) matrix, H represents a p × n (loading) matrix. The number of reduced components p is significantly less than n (p ≪ n). W comprises the solution vectors (the rows of input matrix X), and H stands for the coefficient matrix comprising the objective vectors (the columns of input matrix X). For the elements of W and H, a non-negativity constraint is introduced, which allows positively correlating objectives only and creates non-orthogonal basis vectors. The constraint can also be perceived as vectors the solutions are placed between   establisH determine how similar an objective is to a component. W returns the weights/scores of the solutions. The weights and coefficients matrices are multiplied to reconstruct the original matrix. The clustering happens naturally when the matrix is decomposed. The Pareto front placed on the reduced data points is also clustered.As presented in W and H matrices, the approximation is done through two equations that are iteratively solved.z denotes the index of iteration, and T stands for transposing a matrix.To efficiently calculate the W(0), H(0)) are filled with random numbers in the worst-case scenario and the equations approximate X iteratively by using the newest iterate of the other lower rank matrix.The starting matrices  causes the Pareto fronts to be no longer crowded by the non-dominated solutions. The efficiency of the Pareto dominance-based search is reassured, and the visualization problems are solved. Only a positive correlation between the objectives is allowed due to the non-negativity constraint. Therefore, the optimization only considers the Pareto front of positively correlating objectives. The two or three dimensions determine the components that group different objectives.By reducing the X for the projection can be arbitrary, depending on the purpose of its application: the normalized form of the matrix provides information on the relationship between the solutions, while its ranked form describes the relationship between the ranked objectives.The choice of input matrix Our motivation is to remove irrelevant variables to improve the clarity of the analysis. Thus, NMF is further constrained with sparsity, which aims to represent the original dataset with the least amount of objectives. In other words, the aim is to reduce the number of objectives so that Pareto fronts can be applied and be coherent. Sparsity measures how many zero values are located in a matrix. In sparse NMF, the coefficients and weights are constrained to contain a number of zeroes based on regularization parameters. In a sparse matrix, the non-zero values can effectively represent the solutions, a consequence of which is the emphasis of non-dominated objectives. The constraining vectors that define non-negativity require the solutions to be placed on them, or in their vicinity. The required accuracy determines sparsity. By enforcing a certain sparsity, only the dominant objectives are defined in each constraint vector. The effect of sparsity is depicted in part C of W and H:α denotes the regularization parameter of sparsity in W, while β enforces a sparseness constraint for each hk, k = 1, …, n columns of the H coefficient matrix to ensure sparseness of the model.The usage of sparse non-negative matrix factorization (sparse NMF) , 4 is prL2) two-norm calibration or maintenance without reference solutions . The smaller the entropy is, the more similar the values are, reducing the overall efficiency of the ranking and hindering the selection process of optimal solutions.Rankings should contain as many different ranks as possible to distinguish the solutions. The number of tied ranks should be minimal, which can be measured with the help of relative entropy that describes the distribution of the ranking [vice versa. The algorithm provided by Yi Cao [The hypervolume  is used y Yi Cao  estimatey Yi Cao  simulaticurse of dimensionality.The reason behind employing these measures is to evaluate the Pareto front. High hypervolume indicates an inaccurate Pareto front as considered a far cry from the ideal front. Low RE can be associated with over-saturated Pareto fronts that demonstrate the ab ovo, is biased towards the English-speaking scientific community, e.g., the start of some rankings based on their data mainly includes institutes from English-speaking countries.We employ the CWTS Leiden Ranking 2020 database , which ii.e., the ranks of the university according to the objectives) is used as a reference, and no preprocessing was performed on the data.Two different methods are applied to preprocess the data. First, the min-max algorithm scales the objectives to the interval of zero and one, referred to as scaled input. The other method was ranking with tied ranks where the arithmetic means are designated to each solution (university) with equal footing (ranked input). Additionally, in the SRD analysis, the maximum value of each row  = 0.7925).Concerning many-objective rankings, several questions may arise: How can ranking be fair? By being naïve and in the name of equity, considering all institutions firsts? Should all the positive and negative aspects of a university be considered? From the viewpoint of MaOO, if all solutions are optimal, it is not an optimization problem. However, if all objectives are incorporated to provide an agglomerated ranking, each solution might be the best from a specific viewpoint. In this case, though everyone is a first, a comprehensive ranking cannot be disclosed. In When the number of publications increases, more and more scientists decide to self-archive their publications. As these publications are accessible to everyone, a publication may target a wider audience, not just those who subscribe to the relevant journals. These two objectives positively correlate with each other. If more researchers are familiar with the work of the particular author, the higher the chance of collaboration or citing/referencing is. Even though these objectives produce the most distinctive ranking, a contradiction is proposed as one of the most correlating objective pairs is chosen. The non-dominated solutions in decreasing order of optimality are as follows: Harvard University, University College London, University of Oxford, John Hopkins University (the latter two are on the same front), and the University of Cambridge. The Pareto fronts are visually separated.many-objective problems is that differentiating between the solutions is impossible, which is measurable with the relative entropy. On the other hand, when all the objectives are used as inputs, the ranking inflates, and the curse of dimensionality appears, resulting in all solutions being placed on the first Pareto front. In this case, the relative entropy is zero. The smaller it is, the more saturated the ranking and the fronts are. As a result, the following question remains: how is it possible to determine the best when all universities are on the front?The hardship of optimizing RE(D) = 0.6072.The introduced dimensionality reduction-based techniques can solve the problem of the inflated ranks of many-objective ranking. The main idea is to reduce the number of dimensions while simultaneously retaining as much information on the original data as possible. Although the performance of NMF and sparse NMF is presented in this paper, the traditional PCA is also applied to provide a benchmark for the performance of dimensionality reduction, as well as present the correlation structure of the objectives. Only the first two principal components (PCs) were selected, which explains 65.87% of the total data variance. The Pareto fronts of the objectives are determined in this reduced objective space as presented in The loading vectors form six major clusters in After discussing the main driving clusters of the first two PCs, the variables in the positive quarter of the Cartesian coordinate system are left unexplained. These variables form two major clusters, and an outlier, namely S8, the ‘average number of citations of the publications of a university’. The first cluster is the proportional objectives of collaborations (C7-C11) and green open-access publications (O12), except the proportional short-distance collaboration indicator, which is almost inversely proportional to this cluster. By publishing free information, the university reaches a larger and more diverse audience. In other words, more ideas may be generated. Moreover, if a collaboration is initiated, the rank of the institution might benefit significantly from the new accredited publications. The second cluster consists of the proportion of the most cited publications of universities (S9-S13). The following statements might be trivial, but the cluster gathers descriptors, which lead to the proposition that the more negligible impact of the publications produced by an institution is, the poorer the performance. The proportion of scientific variables (S8-S13) shows that innovation is one of the central roles of academic institutions; the ranking is heavily influenced by publications with higher degrees of impact and the average number of publications. However, it should be emphasized that the point cloud cannot be so nicely interpreted as for loadings. Mirroring the blue dots (at the end of the lines) through the origin preserves the opposite signs for loadings. The optimal solutions are considered for both negatively and positively correlating objectives. Hence, it seems necessary to compress all points and all information into the first quarter of the Cartesian coordinate system. The random and reversely ranked objectives should be determined using SRD.In the present work, the SRD is the perfect tool to determine the relationship between different objectives, hence underpin, validate and interpret the results of the NMF-based solution. The application of the SRD provides a list of objectives that are ranked closely, randomly, or reversely to the ideal rank.In terms of positive ranking, the ‘wrong’ objectives are located on the right-hand side of the XX19 line (randomly or reversely ranked indices). Moreover, similarly, the ‘good’ objectives are on the left-hand side of the XX1 line. The randomly ranked objectives can be identified between the two lines. The first five of both ‘good’ and ‘bad’ objectives are provided in As some objectives in the PCA components negatively correlate to the other positive objectives, the Pareto front is selected along both negatively and positively correlating objectives which invalidates the ranking.RE(D) = 0.5131. It is also important to note that the components are maximized, as no negative correlation between the indicators can be established due to the non-negativity constraint.With Non-negative matrix factorization, both the original and ranked data are examined. In i.e., Japan. Germany and the United States cannot be distinguished properly, as their institutions are scattered throughout the West cluster. Some universities, such as Royal Holloway, and the University of London, are located much closer to the left-hand side cluster due to their willingness to enroll many students from diverse cultural backgrounds. Brandeis University focuses on liberal arts and offers various cultural studies. The Pareto fronts are clustered to provide a vivid multicultural ranking, where nationality is accounted for without distorting the ranking.The method inherently clusters the universities into institutions under Asian (mainly Chinese) and Western cultures. The brown diamonds and red stars’ cluster on the left-hand side contains Chinese and Taiwanese institutions. The South Korean universities appear first as a subcluster of the second bigger cluster by proceeding further right. Under that subcluster are the Iranian institutions close to the lower-ranked Pareto fronts. The universities above can be regarded as under the influence of both cultures found in Western and Eastern-style institutions. The institutions influenced by the West cover the remaining space as well as some subclusters are created by nations, e.g., open-access publications may promote international collaborations, or some international funding opportunities encourage cooperation and open-access as performance indicators. The proportional collaboration indicators, namely, short-distance (C10), long-distance (C11), and industry (C9), are much more closely related than in PCA. Proportion of the top 1% cited (S10) is an outlier in this representation but similarly separates the proportional and quantitative indicators as in the PCA-based projection. The cluster under S10, consists of proportion of male authorships (G9), average number of citation (S9) and proportional scientific impact indicators S11–13. It suggests that the ranking of the male-to-female ratio weakly correlates to the proportional indicators of the 5–50% scientific impact and the average number of publications, which may be accredited to women being ill-represented in some highly-citing fields.If the ranked matrix is chosen as the input of NMF, it provides information on the relationship between the ranks and ranked objectives of the universities. The biplot in On the other hand, the next cluster  consists of female authorships and the quantity of hybrid, bronze, and green open-access publications. The remaining quantitative objectives are located somewhat close to each other, as is the case in PCA. These objectives form the intermediate components of the SRD values by connecting the outlier one with the best/worst objectives. The relationship between the objectives of the upper clusters is similar. NMF selected the same order of objectives that SRD chose for the first five in both directions. The best objectives in terms of ranking were placed in the vicinity of the first component. Given the non-negativity constraint, all objectives correlate positively.Sparsity on the scaled data was enforced to see what non-dominated factors represent the dataset. The ‘worst’ objectives were removed, and as a result, the components changed their identity to proportional and quantity components. The front itself consists of institutions from the Anglosphere. It is important to note that even if these are exceptional institutions in terms of both components, the following Pareto fronts are filled with academies of Asian and other nationalities. A vital part of the sparse NMF method is the regularization parameters, selected to be 0.4 each. These balance the relative entropy and hypervolume to some extent; therefore, the determination may be possible with the help of the performance metrics, but further research is required for an exact iterative algorithm. As such, the number of the bests can be influenced, and the accuracy can be determined as to how many of the solutions should be placed on the Pareto front.It is important to note that the benefits of the sparse NMF are the customization of the number of members of the Pareto front and a more precise representation of the components (as most objectives are present only as a part of one component).Effects of the size and echo inherently distort the reliability of the results. In other words, more prominent universities have an advantage in terms of almost every objective indicator. Moreover, a once prestigious university will be highly regarded for many years to come, even if it can no longer maintain its outstanding performance. The rankings based on different objectives agree  in terms of the leaders and drivers (the end of the ranking), while in the center region of the different rankings, the difference in relative ranking is often only visible through a magnifying glass , 47. TheThe indicator of ‘unknown gender’ is outstanding in the case of new universities inhibited by possibly smaller ethnicities. They may have a higher degree of uncertainty concerning the gender of their researchers, most likely determined by automated engines. It is a possibility that their universities are not ranked amongst the top universities due to their young history and the lack of publication records. Hence, the “unknown gender” indicator ranks in the reversed order.The extended TOPSIS method  is capabe.g., if rank transformation is applied to the data. The results return the relationship of the ranks, rather than the data, though the data is affected through a layer of abstraction. Besides, no ‘hierarchy’ exists in NMF; the clustered weights provide a Pareto front to be evaluated. It is important to note that WTC is not a method proposed to solve MaOO but rather an identification system for academics; therefore, comparing the two methods is to be handled accordingly.The only downside of the Weighted Top Candidate (WTC) selection method  is that Compared to the University Profiling Network (UPN) , NMF is The trade-off ranking method  is sensiThe RE of the dataset, the highest entropy objective-pairs, and each method seen are presented in i.e., the first Pareto front is constructed from only one solution. In this case, the two chosen objectives are almost directly proportional. However, in this case, the other objectives are not considered, and by adding them, the problem of the curse of dimensionality returns. The NMF with the scaled input matrix is outperformed by all other dimensionality reduction-based techniques. Although PCA performs better in determining the ideal Pareto fronts as reflected by its lower hypervolume value, its drawback is that inversely correlated objectives are merged in its components, i.e., with the cost of losing the interpretable and positively correlating objectives, PCA provides a more accurate approximation of the ideal Pareto front. The orthogonal PCA factors are not easy to interpret; similar variables may have different signs, SRD arranges them in 1D and shows the reverse (and random) characters of variables. The NMF groups the original variable in an easily perceivable manner. The clusters of objectives are no longer constructed of conflicting objectives. Sparse NMF provides a better ranking solution than PCA, as it clusters the similarly ranking objectives together . Sparsity can also be calibrated to control the number of solutions on the Pareto front, which comes in handy when the saturation of the Pareto front is to be controlled. Sparse NMF with ranked input has not been included because the ranks or the additive combination of ranks cannot be zero, and enforcing these invalid values reduces the efficiency and validity of the ranking. The relative entropy value of NMF with ranked input is not extraordinary. Yet, the hypervolume is zero, the smallest of the dimensionality reduction methods, providing a good trade-off between the results. Depending on whether the efficiency of the ranking or the closeness to the ideal solution is required, sparse NMF and NMF with ranked input is recommended, respectively. The time required was 0.5 seconds to approximate principal components, and the approximation of lower rank matrices (W1176×2 and H2×46) required 0.3267 seconds with an Intel Core i7–6700HQ 2.60GHz processor and 8 gigabyte RAM. NMF with ranked input was executed in 0.0389 seconds due to positive integers, and sparse NMF with removed objectives was performed in 0.3507 seconds.The Pareto front of the original dataset includes all universities; thus, the relative RE and hypervolume values are close to zero. The selection of the pair of objectives with the highest entropy provides an ideal solution as it efficiently determines the best university, The finding implies that the NMF can successfully solve MaOO problems. The non-negative objective space is reduced to the desired number of components that supports the generation of easily interpretable Pareto fronts. The proposed SRD-based filtering of the objectives is beneficial to remove non-informative and reversely-ranked objectives.This work focuses on ranking based on numerical values and their objective functions. Of course, subjectivity may influence the analysis in various ways; the most obvious is the selection of weights and redundant objectives, which deteriorates the analysis by showing the group of criteria more important than it is. The reason behind using the non-negative matrix factorization (NMF) is to reduce human subjectivity by automatically selecting matching objectives in one component, while reducing redundancy and removing subjective weighting of the objectives.Any dataset may contain several objectives. The redundancy influences the weight of the objective groups. If the group comprises mostly irrelevant variables, the weighting will incorrectly shift toward that group. Without modification, the sum of ranking differences (SRD) and NMF can explore the connection between the objectives, thus, helping compare the indicators and sort out redundant ones.not recommended, neither is increasing the number of co-authors. This empirical investigation exposes the conservatism of the academia.The results of applying the NMF does not simply confirm the trivial view that the number of publications and visibility criteria (number of citations) are essential to yield a better ranking, but also establishes some objectives ranked in reverse order, which should be avoided or diminished: publications without DOIs, low male-to-female ratio, short distance cooperation. A recommended publication policy would include complete utilization of open-access possibilities and long-distance collaborations. Naturally, diminishing the female contributions is Similarly, the empirical clustering of Asian and Western universities along with the ‘in-between’ does not involve any value judgment but unambiguously shows the different outcome and clustering of the universities. It should also be emphasized that the intermediate position of the top 1% and 5% citations suggest that the role of these indices are over-valued, and they are predominantly determined by random factors. It is reassuring that the analyses return well-known tendencies and commonplaces. The latter reinforces our surprising or uncommon conclusions, make them plausible.Only coupling of sum of ranking differences and non-negative factorization is able to group the variables indicating cultural differences, at least which are present in the publishing culture. The y axis of many-objective optimization. Methods such as principal component analysis (PCA), NMF, and sparse NMF can determine a proper ranking even if the objectives are controversial and rank in reverse. The sum of ranking differences is a handy tool in terms of presampling as it detects unfavourable objectives (random and reversed ranking) that can be later removed.This paper provides a complex analysis of ranking with high dimensionality and In university rankings, the grouping of universities by NMF is heavily influenced by national publication policies, namely clusters, depending on where a university belongs in these (inter)national communities. The thorough analysis of the objectives returned trivial statements that the number of publications and citations is a driving force for a higher rank. However, some not-so-trivial results may help to develop an advanced plan for improving publication policy. The endorsement of long-distance and international collaborations is proposed. To promote the latter, an increase in the number of open-access publication may result in many collaborative publications.many-objective optimization, only a non-negativity constraint needs to be met. The method provides positively correlated objectives and clusters the data (here universities) and the Pareto front. To answer the question of ‘how can ranking be fair?’, we propose the implementation of SRD for filtering and sparse NMF to select universities from throughout the world. Although we discussed the university ranking on an individual dataset, the methodology presented above is entirely general and any dataset that contains meaningful information can be reduced using them. Whereas PCA is hard to interpret, the SRD method orders the objectives, while NMF clusters the objectives, and sparsity removes the weak and redundant objectives from the analysis.By employing NMF for S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.S4 Appendix(PDF)Click here for additional data file.