Separate Dimensions Research Articles

In the zone: Work at the intersection of trade and migration

Abstract Trade and immigration are generally described as separate dimensions of globalization. This Article challenges that story by focusing on settings where states and private actors are bringing the two together to achieve disparate economic and policy goals. In one of the two sets of cases analyzed here, governments in the Global South are seeking to increase trade through the use of migrant labor, attracting transnational firms to export manufacturing zones by importing lower-cost workers from other countries. In the other, policymakers in the Global North are seeking to decrease immigration through the use of trade by investing in export processing zones in migrant origin countries, on the theory that more trade, and the employment it creates, will deter onward migration to the Global North. I use these contexts as the starting point for a reconsideration of core ideas in trade and migration policy and theory. In the first set of cases, governments are constructing a comparative advantage in labor from whole cloth, by bringing in workers from other countries on terms that restrict their freedom and subject them to exploitation at work. This challenges the usual description in the trade literature of labor cost as a natural phenomenon based on local wage and productivity levels, and thus a legitimate source of advantage in trade. Meanwhile, transnational firms that locate production in such export processing zones sidestep the ordinary choice between benefiting from global wage differentials by moving work overseas or by hiring migrant workers. Instead, they do both simultaneously, a strategy I term “double labor arbitrage.” I explore the ways in which the construction of comparative advantage and double labor arbitrage operate together to extract additional value from workers for the benefit of states and corporations. With regard to the second set of cases, I draw on recent empirical economic scholarship to challenge the argument that more trade will decrease emigration. More profoundly, I question the normative justification for these proposals, given the treatment of workers in the zones. Although proposed as a “solution” to immigration, I argue that they are much more likely to deepen the problems that drive it. Immigration and trade law do not bar any of the developments I analyze here. The question is whether they should. This Article—the first step in a larger project—launches that inquiry.

Effects of sociodemographic and health factors on the self-management of non-communicable diseases among Chilean adults during the Covid-19 pandemic.

Individuals with non-communicable diseases (NCDs) are potentially at increased vulnerability during the Covid-19 pandemic and require additional help to reduce risk. Self-management is one effective strategy and this study investigated the effect of sociodemographic and health factors on the self-management of some non-communicable diseases, namely hypertension, type 2 diabetes mellitus and dyslipidemia, among Chilean adults during the Covid-19 pandemic. A cross-sectional telephone survey was carried out on 910 participants with NCDs, from Santiago, Chile. An adapted and validated version of the "Partners in Health" scale was used to measure self-management. Exploratory Factor analysis yielded five dimensions of this scale: Disease Knowledge, Healthcare Team Relationship, General Self-Management and Daily Routines, Drug Access and Intake, and Monitoring and Decision-Making. The average of these dimensions was calculated to create a new variable Self-Management Mean, which was used as a dependent variable together with the five separate dimensions. Independent variables included age, gender, years of schooling, number of diseases, the percentage of Multidimensional Poverty Index in the commune of residence, and self-rated health status. Beta regressions and ANOVA for the Beta regression residuals were utilized for analyses. Beta regression model explained 8.1% of the variance in Self-Management Mean. Age, years of schooling, number of diseases and self-rated health status were statistically associated with Self-Management Mean and dimensions related to daily routines and health decision making, such as Disease Knowledge, General Self-Management and Daily Routines, and Monitoring and Decision-Making. Gender and the percentage of Multidimensional Poverty Index in the commune of residence were insignificant. Strategies for self-management of NCDs during a crisis should consider age, years of schooling, number of diseases, and self-rated health status in their design.

Task and Relationship Conflicts, Employee Agility, and Perceived Job Performance

The investigation into the impact of conflict on the modern approach of employee agility as well as employees’ perceived job performance seems to be a critical topic that has not relatively been touched on yet. The present study aims to look at the influence of conflict on employees’ agility and employee’s perceived job performance. This research divides conflict into two separate dimensions as task and relationship conflict to investigate the impact of each on agility and job performance. The data is collected by employing a self-structured questionnaire using 23 items and a 5-point Likert scale from the employees of a hygiene and sanitation company in Cameroon. The proposed model is examined via PLS-SEM. Overall, the study reveals that there is a negative coefficient in the relationship between relationship conflict and employee’s agility; a significant positive relationship between task conflict and employee agility; and a positive significant relationship between employee’s agility and employee perceived job performance. The generated results validate that task conflict is vital and welcome to workers; thus, managers are expected not to blindly avoid conflicts. The consequence of a constructive conflict can be employee agility which in itself will lead to a high level of job performance.

Accurate classification of white blood cells by coupling pre-trained ResNet and DenseNet with SCAM mechanism

BackgroundVia counting the different kinds of white blood cells (WBCs), a good quantitative description of a person’s health status is obtained, thus forming the critical aspects for the early treatment of several diseases. Thereby, correct classification of WBCs is crucial. Unfortunately, the manual microscopic evaluation is complicated, time-consuming, and subjective, so its statistical reliability becomes limited. Hence, the automatic and accurate identification of WBCs is of great benefit. However, the similarity between WBC samples and the imbalance and insufficiency of samples in the field of medical computer vision bring challenges to intelligent and accurate classification of WBCs. To tackle these challenges, this study proposes a deep learning framework by coupling the pre-trained ResNet and DenseNet with SCAM (spatial and channel attention module) for accurately classifying WBCs.ResultsIn the proposed network, ResNet and DenseNet enables information reusage and new information exploration, respectively, which are both important and compatible for learning good representations. Meanwhile, the SCAM module sequentially infers attention maps from two separate dimensions of space and channel to emphasize important information or suppress unnecessary information, further enhancing the representation power of our model for WBCs to overcome the limitation of sample similarity. Moreover, the data augmentation and transfer learning techniques are used to handle the data of imbalance and insufficiency. In addition, the mixup approach is adopted for modeling the vicinity relation across training samples of different categories to increase the generalizability of the model. By comparing with five representative networks on our developed LDWBC dataset and the publicly available LISC, BCCD, and Raabin WBC datasets, our model achieves the best overall performance. We also implement the occlusion testing by the gradient-weighted class activation mapping (Grad-CAM) algorithm to improve the interpretability of our model.ConclusionThe proposed method has great potential for application in intelligent and accurate classification of WBCs.

Relating psychiatric symptoms and self-regulation during the COVID-19 crisis

Disruptions of self-regulation are a hallmark of numerous psychiatric disorders. Here, we examine the relationship between transdiagnostic dimensions of psychopathology and changes in self-regulation in the early phase of the COVID-19 pandemic. We used a data-driven approach on a large number of cognitive tasks and self-reported surveys in training datasets. Then, we derived measures of self-regulation and psychiatric functioning in an independent population sample (N = 102) tested both before and after the onset of the COVID-19 pandemic, when the restrictions in place represented a threat to mental health and forced people to flexibly adjust to modifications of daily routines. We found independent relationships between transdiagnostic dimensions of psychopathology and longitudinal alterations in specific domains of self-regulation defined using a diffusion decision model. Compared to the period preceding the onset of the pandemic, a symptom dimension related to anxiety and depression was characterized by a more cautious behavior, indexed by the need to accumulate more evidence before making a decision. Instead, social withdrawal related to faster non-decision processes. Self-reported measures of self-regulation predicted variance in psychiatric symptoms both concurrently and prospectively, revealing the psychological dimensions relevant for separate transdiagnostic dimensions of psychiatry, but tasks did not. Taken together, our results are suggestive of potential cognitive vulnerabilities in the domain of self-regulation in people with underlying psychiatric difficulties in face of real-life stressors. More generally, they also suggest that the study of cognition needs to take into account the dynamic nature of real-world events as well as within-subject variability over time.

Simulating comprehensive two-dimensional gas chromatography mass spectrometry data with realistic run-to-run shifting to evaluate the robustness of tile-based Fisher ratio analysis

Untargeted analysis of comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) data has the potential to be hindered by run-to-run retention time shifting. To address this challenge, tile-based Fisher ratio (F-ratio) analysis (FRA) has been developed, which utilizes a supervised, untargeted approach involving a chromatographic segmentation routine termed "tiling" combined with the ANOVA F-ratio statistic to discover class-distinguishing analytes while minimizing false positives arising from shifting. The tiling algorithm is designed to account for retention shifting in both separation dimensions. Although applications of FRA have been reported, there remains a need to thoroughly evaluate the robustness of FRA for different levels of run-to-run retention shifting in order to broaden the scope of its application. To this end, a novel method of simulating GC×GC-TOFMS chromatograms with realistic run-to-run shifting is presented by random generation of low-frequency "shift functions". The dimensionless retention-time precision, <δr>, which is four times the standard deviation in retention time normalized to the peak width-at-base is used as a key modeling variable along with the 2D chromatographic saturation, αe,2D, and within-class relative standard deviation in peak area, RSDwc. We demonstrate that all three of these variables operate together to impact true positive discovery. To quantify the "success" of true positive discovery, GC×GC-TOFMS datasets for various combinations of <δr>, αe,2D, and RSDwc were simulated and then analyzed by FRA using a wide range of relative tile areas (RTA), which is a dimensionless measure of tile size. Since each hit in the FRA hit list was known a priori as either a true or false positive based on the simulation inputs, receiver operating characteristic (ROC) curves were readily constructed. Then, the area under the ROC curve (AUROC) was used as a metric for discovery "success" for various combinations of the modeling variables. Based on the results of this study, recommendations for tile size selection and experimental design are provided, and further supported by comparison to previous tile-based FRA applications. For instance, values for <δr>, αe,2D, and RSDwc obtained from a GC×GC-TOFMS dataset of yeast metabolites suggested an optimum RTA of 6.25, corresponding closely to the RTA of 4.00 employed in the study, implying the simulation results obtained here can be generalized to real datasets.

Relationship between generic instruments used to measure healthrelated quality of life same or different

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The Swedish research Council for Medicine and health - VR Swedish Research Council for health, working life and welfare - FORTE Background Health-related quality of life (HRQoL) is one and possibly the most important follow-up measure that is used to assess outcomes after long term and critical illness, such as heart failure. There is today an abundance of available generic measures relevant for all types of diseases and health states, and therefore suitable in heterogenous patient cohorts. Globally, two of the most common generic measures of HRQoL in use today are the EuroQol-5D (EQ-5D) and the RAND-36/36-item short-form SF-36. Another commonly used instrument is Cantril’s ladder of life. With the purpose to create a valid but not burdensome survey before and after an intervention in a cohort of former critically ill patients after a period of cardiac care, the question was raised what instrument to choose, and if the instruments correlates. If so, not all items have to be used if they measure the same outcome. Purpose To examine the relationship between different generic instruments for HRQoL in patients with heart failure. Methods For EQ-5D the separate five separate dimensions, the index from the combined dimensions, and a visual analog scale (VAS) can be used. For RAND-36 the eight dimensions created from 35 of the 36 questions, and the extern item as a single item called "Health Transition" can be used. Finally, for the Ladder of life the three questions can be used as separate items. The items chosen and measured in a cohort of former critically ill cardiac patients (n=246) were EQ-5D VAS, EQ-index, RAND-36 health transition question, and ladder of life question 1 and 3. Higher scores indicates higher perceived HRQoL on the scales except for RAND-36. The patients had a mean age of 66 years (SD 11.8), 67.3% (n=165) were men. The most common diagnoses in the sample were heart failure 68% (n=148), diabetes 22% (n=31), cancer (17% (n=36), and pulmonary disease 14% (n=31). The Spearman´s rank correlation was used to analyze the strength of association in the nonparametric data. Results Up to moderately strong correlation was seen between the items (see table). The strongest correlation was found between the EQ-5D VAS and RAND-36 Health Transition item (-0,629 (p=0.01)), followed by both RAND-36 and EQ-5D VAS and the ladder of life question "Where on the ladder do you feel you personally stand right now" (0,615 (p=0.01)) and (0.606 (p=0.01)) respectively. The two ladder of life questions correlated moderately (0.689, (p=0.01)). Conclusions Albeit the same focus area, the chosen general instruments, and items for measuring HRQoL was not strongly correlated indicated that they do not measure the exact same concepts and dimension. This is of importance when chosen instruments for HRQoL follow-up. There might be a need to combine more than one generic instrument considering the balance between the burden for the patient and the purpose and depth of the investigation. Further conceptual consideration regarding to these different generic instruments are warranted.

Gösterişçi Tüketim Davranışı Ekseninde Yeni Orta Sınıfların Tüketim Pratikleri

Today, individuals live in a daily life space where they are addicted to fashion and what is constantly renewed. Consumption practices, guided by the symbolic values of consumption objects, have been a means of class mobility for individuals in different income groups as a status indicator. Certain types of consumption practices have become the means and purpose of creating a lifestyle, ensuring its continuity, and creating differences by making use of the artificial attractiveness of consumption objects. The motivation to be different from the other has become possible with consumption practicies that are reproduced, valued and given meaning, which have the potential to indicate the social position, class position and prestige of the individual. In this article, the dynamics and dimensions of differentiation and separation forms embodied by consumption practices are discussed.

Metafilozofia negatywistycznej metafizyki unitarnej Leszka Nowaka

The theme of this paper is Leszek Nowak’s reconstruction and systematization of metaphilosophy of the negativistic unitarian metaphysics described in Being and Thought in four volumes, published in the years 1998, 2004, 2007, 2019. The goal of the article is to contribute to the discussion on the status of metaphilosophy. Metaphilosophy is understood here as a separate dimension of every philosophical conception determining, on the one hand, how we understand the realm of objects (what we postulate), on the other hand, how we choose methods for the construction of theories and ways to legitimize them. The author tries to show that Nowak’s metaphilosophy assumed in negativistic unitarian metaphysics meets exactly this definition of metaphilosophy.

Nationalism in the ‘Nation of Immigrants’: Race, Ethnicity, and National Attachment

Abstract This paper compares nationalist attitudes among Whites, Latinos, and African Americans. The research on nationalism and national attachment draws varied conclusions about how race and ethnicity structure such attitudes; some find that Whites have the strongest views, while others see more similarities than differences. Using the General Social Survey of 2014, we examine three separate dimensions of nationalism: American nationalism, American national identity, and American national pride. We test for differences across race and ethnicity as well as how such attitudes structure opinions about immigrants. Despite some expectations in the literature that views might vary by group, we generally find (albeit with some complexities) “minimal effects” of race and ethnicity. Latinos, Blacks, and Whites agree with the three nationalism measures at similar levels, despite the very different national histories of each group. This is consistent with work finding “a great deal of consensus on the norms, values, and behaviors that constitute American identity” (Schildkraut 2007. “Defining American Identity in the Twenty-First Century: How Much “There” Is There?”.” The Journal of Politics v69 (3): 597–615, 605). In addition, while nationalism is associated with immigration opinions, such effects are predominantly among Whites and African Americans and relatively weak for Latinos.

Disentangling social capital – Understanding the effect of bonding and bridging on urban activity participation

Social capital is a critical glue for economic and social development in urban areas. Yet, to effectively guide research and practice, there is a need for careful measurement of social capital and how it links to important aspects of urban system functions. This study is aimed at examining the multi-dimensional nature of social capital and the relationship between these dimensions and travel behavior. Prior research has shown connections between stand-alone social capital concepts, such as resources gathered via social networks, with specific aspects of travel behavior. In this work, we expand the definition of social capital to cover separate dimensions, modeled via multiple indicators. Specifically, we make use of over 1400 observations from the Pew Internet Networks and Community Survey dataset to build a Structural Equation Model dividing social capital into two latent dimensions: bonding and bridging to examine the relationship of both these dimensions with discretionary urban activity participation diversity and frequency. Moreover, broader measures of neighborhood and community engagement are included in the model to explain how such engagement can help with the accumulation of social capital. Our results indicate a positive but differential relationship between both social capital dimensions and activity participation. Further, the results also suggest an absence of correlation between bonding and bridging capital, strengthening the hypothesis that social capital is multi-dimensional. In terms of explaining the social capital accrual, we find that while community engagement is positively correlated to bridging capital, no evidence was found for a relationship between community engagement and bonding capital. Further, neighborhood engagement was not found to be associated with any of the social capital dimensions. This suggests that individuals predominantly rely on close-knit and stronger relationships for social/emotional support, while instead, community engagement significantly helps in the accumulation of bridging capital. The result from the study can be used by policy makers to improve transportation planning, management, and community well-being.

An off-line three-dimensional liquid chromatography/Q-Orbitrap mass spectrometry approach enabling the discovery of 1561 potentially unknown ginsenosides from the flower buds of Panax ginseng, Panax quinquefolius and Panax notoginseng

To comprehensively elucidate the herbal metabolites is crucial in natural products research to discover new lead compounds. Ginsenosides are an important class of bioactive components from the Panax plants exerting the significant tonifying effects. However, to identify new ginsenosides by the conventional strategies trends to be more and more difficult because of the large spans of acid-base property (the neutral and acidic saponins), molecular mass (400–1400 Da), and rather low content. Herein, an off-line multidimensional chromatography/high-resolution mass spectrometry approach was presented: ion exchange chromatography (IEC) as the first dimension of separation, hydrophilic interaction chromatography (HILIC) in the second dimension, and reversed-phase chromatography (RPC) for the third dimension which was hyphenated to a Q Exactive Q-Orbitrap mass spectrometer. By applying to the flower buds of P. ginseng (PGF), P. quinquefolius (PQF), and P. notoginseng (PNF), IEC using a PhenoSphereTM SAX column could fractionate the total extracts into the neutral (unretained) and acidic (retained) fractions, while HILIC (an XBridge Amide column) and RPC (BEH Shield RP18 column) achieved the hydrophilic interaction and hydrophobic interaction separations, respectively. Q-Orbitrap mass spectrometry offered rich structural information and complementary resolution to the co-eluting components, particular to those minor ones by including precursor ion lists in data-dependent acquisition. We could characterize 803 ginsenosides from PGF, 795 from PQF, and 833 from PNF, and 1561 thereof are potentially unknown. These results can indicate the great potential of this multidimensional approach in the ultra-deep characterization of complex herbal samples supporting the efficient discovery of potentially novel natural compounds.

Spin separation in time dimension for electron in magnetically and electrically confined semiconductor nanostructure

We calculate dwell time of electron in magnetically and electrically confined semiconductor nanostructure (MECSN), which can be experimentally fabricated by patterning a ferromagnetic (FM) stripe and a Schottky metal (SM) stripe on top and bottom of InAs/AlxIn1-xAs, respectively. Spin-dependent dwell time is found thanks to the Zeeman interaction, which gives rise to the separation of electron spins in time dimension. Properly patterning the SM stripe can obviously improve electron-spin polarization owing to the SM-stripe dependence of effective potential experienced by electron in the MECSN, achieving a tunable temporal spin splitter for semiconductor spintronics device applications.

An empirical evaluation of future auditors in the U.S.A. and India using the trifurcated dimensions of trait professional skepticism

PurposeUnderstanding the factors that impact the audit quality of work performed by affiliated offshore entities has become imperative for US accounting firms. The purpose of this paper is to gain a better understanding of the role that cultural differences have on the trait professional skepticism mindset of future auditors in the USA and India.Design/methodology/approachThe authors use the Hurtt (2010) Professional Skepticism Scale (HPSS) to evaluate the role that culture has on the trait professional skepticism mindset of a sample of future auditors in the USA and India.FindingsThe authors identify three distinct dimensions of trait professional skepticism embedded in the HPSS. The research finds no significant differences between USA and Indian auditing students on the evidential “trust but verify” dimension of trait professional skepticism; however, US students score higher on the behavioral “presumptive doubt” and self-reliance dimensions.Practical implicationsGiven culture significantly influences trait professional skepticism, firms and regulators should be highly cognizant of the type of work that is being sent offshore. Firms using affiliated offshore entities should also ensure that robust integration practices are used to facilitate the level of professional skepticism necessary to perform a quality audit.Originality/valueBy identifying three separate dimensions in the HPSS, the research takes an important step in understanding the factors that impact the quality of audit procedures performed in a critical affiliated offshore entity for US-based accounting firms.

Environmental Policy Preferences and Economic Interests in the Nature/Agriculture and Climate/Energy Dimension in the Netherlands

AbstractThe idea that citizens' support for environmental policies depends on their economic interest and the community that one lives in, has been debated extensively in the environmental attitudes literature. However, this literature has not differentiated between separate policy dimensions that concern measures that affect specific groups in different ways. This paper differentiates between a nature/agriculture dimension that divides those who prioritize the agrarian interest from those who prioritize the protection of nature and a climate/energy dimension that divides those who prioritize industrial interest from those who prioritize fighting climate change, using a new survey in the Netherlands (N = 11,327). This two‐dimensional model meets three criteria: scalability, validity, and utility. Scalability is shown by factor analysis and Mokken scaling. Validity is shown by regression analyses that show that whether one lives in a rural or an urban community predicts one's position on the nature/agriculture dimension and that one's financial security predicts one's position on the climate/energy dimension. The utility is shown by regression analyses where the two dimensions are used to predict voting behavior. The Green Party voters favor nature and climate protection, the Liberal Party voters have the opposite views, the Christian‐Democrats favor agricultural interests and the Freedom Party favor industrial interests.

Gods, Games, and Globalization: New Perspectives on Religion and Sport

Gods, Games, and Globalization: New Perspectives on Religion and Sport

“I Have Never Felt More Utterly Yours”: Presence, Intimacy, and Long-Distance Marriages in the First World War

AbstractThe unique anxieties experienced by married couples remain under-examined aspects of both the First World War and the early twentieth century. Drawing on the writings of four upper-middle-class couples, this article reveals the complex ways in which couples sought to maintain intimacy across transnational time and space during the war. The author argues that, elements of modern marriage were clearly present in these relationships. Wartime separation gave couples space to develop new forms of intimacy and affection. Through creative, often abstract, alternatives developed to affect a sense of presence, spouses were able to know, embody, and imagine one another. While separated couples frequently desired and imagined physical reunion, its fleeting nature was emotionally wearing and often undermined intimacy and togetherness in immediate and long-lasting ways. Exploring the subjective experiences of these couples challenges the tendency to periodize marriage in distinct categories such as patriarchal or companionate, and also invites us to reframe our understanding of the spatial dimensions of separation and intimacy.

Dealing with Wandering First-Dimension Peaks in 2D-LC Separations

The retention of large molecules, such as peptides and proteins, is strongly dependent on mobile-phase composition and temperature. Consequently, the retention times (tR) of these molecules often fluctuate from one separation to the next. In two-dimensional liquid chromatography (2D-LC), this fluctuation can be challenging to deal with, especially if only one or a few specific peaks in the first dimension (1D) separation are targeted for further separation. In this installment, we describe instrumental concepts that are useful for coping with this challenge.

Identification of Urban Functional Areas Based on the Multimodal Deep Learning Fusion of High-Resolution Remote Sensing Images and Social Perception Data

As the basic spatial unit of urban planning and management, it is necessary to know the distribution status of urban functional areas in time. Due to the complexity of urban land use, it is difficult to identify the urban functional areas using only remote sensing images. Social perception data can provide additional information for the identification of urban functional areas. However, the sources of remote sensing data and social perception data differ, with some differences in data forms. Existing methods cannot comprehensively consider the characteristics of these data for functional area identification. Therefore, in this study, we propose a multimodal deep learning method with an attention mechanism to fully utilize the data features of these two modalities and apply it to the recognition of urban functional areas. First, the pre-processed remote sensing images, points of interest, and building footprint data are divided into block-based target units of features by the road network. Next, the remote sensing image features and social perception data features of the target unit are extracted separately using a two-branch convolutional network. Finally, features are extracted sequentially along two separate dimensions, being channel and spatial, to generate an attention weight map for the identification and classification mapping of urban functional areas. The model framework was finally applied to the Ningbo dataset for testing, and the recognition accuracy was above 93%. The experimental results deduce, as a whole, that the prediction performance of the deep multimodal fusion model framework with an attention mechanism is comparatively superior to other traditional methods. It can provide a reference for the classification of urban land use and provide data support for urban planning and management.

Tile-based variance rank initiated-unsupervised sample indexing for comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

Tile-based variance rank initiated-unsupervised sample indexing (VRI-USI) analysis is introduced for comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). VRI-USI analysis addresses the challenge that irrelevant variables can often obscure true chemical variation when using other unsupervised chemometric tools. Implementation of VRI-USI analysis with GC×GC-TOFMS data incorporates the tile-based Fisher ratio (F-ratio) analysis software platform that mitigates the effects of retention shifting in both separation dimensions with an unsupervised variance metric (instead of the F-ratio metric) as the initial step of ranking the hitlist. Next, implementation of k-means clustering, k, per hit using the silhouette metric, Smax, is used to reveal to what extent recurring indexed sample clusters are uncovered. Finally, based upon a probability-based evaluation of how the individual samples cluster throughout the hitlist an unsupervised class membership is revealed. For a JP8 jet fuel dataset spiked with a sulfur-containing analyte mix at 30-ppm, 15-ppm, and neat, clustering by spike level at k = 3 was the most commonly re-occurring set of index assignments, occurring for 11 out of 14 spiked analytes. Upon application of these k-means index assignments to the entire hitlist, all 14 spiked hits had one way ANOVA p-values < 0.05, validating the presumption of classes. Next, application of VRI-USI to a 3-ppm spiked and neat JP8 jet fuel comparison exhibited similar performance to F-ratio analysis for analyte discovery. In the last study, for a dataset of J1800A, JP4, and JP8 jet fuel, each spiked with the sulfur-containing analyte mix at 30-ppm and neat, 453 out of 520 hits in the hitlist exhibited index assignments indicative of fuel type clustering, with the remaining 67 hits having contradictory assignments. Scrutinization of these 67 hits revealed nine hits with “split combinations” in index assignments, whereby the spiked and neat samples for a given fuel were in separate clusters. Eight of these hits were identified as spiked sulfur analytes. Interestingly, these hits also had large Smax indicative of a true sub-cluster. Thus, tile-based VRI-USI analysis appears to be a promising tool for unsupervised multi-class classification studies using GC×GC-TOFMS data.