Unified Models Research Articles

Physical interpretation of neural network-based nonlinear eddy viscosity models

Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high–fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of the main criticisms of neural network-based turbulence modeling. Therefore, it is increasingly demanding to provide physical interpretation of the trained model, which is of significant interest for guiding the development of interpretable and unified turbulence models. The present work aims to interpret the predictive improvement of turbulence flows based on the behavior of the learned model, represented with tensor basis neural networks. The ensemble Kalman method is used for model learning from sparse observation data due to its ease of implementation and high training efficiency. Two cases, i.e., flow over the S809 airfoil and flow in a square duct, are used to demonstrate the physical interpretation of the ensemble-based turbulence modeling. For the flow over the S809 airfoil, our results show that the ensemble Kalman method learns an optimal linear eddy viscosity model, which improves the prediction of the aerodynamic lift by reducing the eddy viscosity in the upstream boundary layer and promoting the early onset of flow separation. For the square duct case, the method provides a nonlinear eddy viscosity model, which predicts well secondary flows by capturing the imbalance of the Reynolds normal stresses. The flexibility of the ensemble-based method is highlighted to capture characteristics of the flow separation and secondary flow by adjusting the nonlinearity of the turbulence model.

Corporate liability and white-collar crime: Comparative review

The aim of the research was to analyze academic approaches to understanding the legal nature of white-collar crime and what crimes it includes; based on this understanding, the model of corporate criminal liability was investigated to place it in various law enforcement contexts. Throughout the article, appropriate research methods have been used, such as: comparative law method, systemic-structural method, formal-legal method. Based on the results of the detailed comparative analysis, it has been established that there are no unified standards or models for both white-collar crime and corporate criminal liability. Furthermore, it has been argued that the concept of fraud (deception) constitutes the key element of white-collar crime and is also the foundation of most corporate crimes. In the conclusions, it is argued that corporate criminal liability in the United States, and to a lesser extent in some European countries (including Ukraine), is a powerful law enforcement tool capable of protecting society from massive crimes as well as deterring corporations from unlawful deviations.

Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks

With the continuous expansion of renewable energy construction, the power system requires a larger-scale flexible dispatchable and controllable resource for power balance. Fully tapping into the power regulation capability of multi-type loads in industrial parks, making them a low-cost flexible dispatchable and controllable resource, is an effective approach to establish power regulation capability at scale in the new power system. However, the control characteristics of multi-type loads in industrial parks vary greatly, and their control delay characteristics, response speed, and sustainable response time are all different. Traditional dispatch and control methods cannot achieve precise control of the massive and multi-type loads in industrial parks. Therefore, this paper establishes unified models for the control characteristics of multi-type loads in industrial parks, quantitatively characterizes their control delay characteristics, start–stop characteristics, and control response speed. Based on this, the aggregated dispatch and control model and dispatch and control strategy for multi-type loads in industrial parks are developed, which provide a predictive control rate for individual loads considering the delay and segmented response characteristics to achieve precise aggregation control of multi-type loads in industrial parks. Simulation results show that the aggregated dispatch and control model and its aggregated dispatch and control strategy achieve precise control of multi-type loads in industrial parks. Flexible dispatchable and controllable loads can provide low-cost power regulation capability for the new power system.

Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers

In recent years, Aspect-based sentiment analysis (ABSA) has received increasing attention from the scientific community for Vietnamese language. However, most previous studies solved various subtasks in ABSA based on machine learning, deep learning and transformer-based architectures in a classification way. Recently, the release of pre-trained sequence-to-sequence brings a new approach to address the ABSA as a text generation problem for Vietnamese ABSA tasks. In this paper, we formulate the Aspect-category based sentiment analysis task as the conditional text generation task and investigate different unified generative transformer-based models. To represent the labels in a natural sentence, we apply a simple statistical method and observation of the commenting style. We conduct experiments on two benchmark datasets. As a result, our model achieved new state-of-the-art results with the micro F1-score of 75.53% and 86.60% for the two datasets with different levels for the restaurant domain. In addition, our experimental results achieved the best score for the smartphone domain with the macro F1-score of 81.10%.

Stable and Efficient Animal Models of the Peritoneal Adhesion

AbstractPeritoneal adhesion (PA) is a widespread complication of surgery arising from physiological problems. Various therapies and medicines for anti‐PA have been studied but lack unified and standard PA models. Various experimental techniques are applied to establish and investigate reliable models for inducing PA in pigs and rats. This study utilizes four scoring systems to evaluate the severity of adhesions. In pigs, U‐shape incision, L‐shape incision, ileum resection, cecum abrasion & peritoneum abrasion, and cecum purse‐string & peritoneum abrasion models are established, to assess the impact of laparotomy incision and double opposed wound on adhesion formation. For rat models, the exposed double opposed wound models are created, including peritoneum resection & cecum abrasion, peritoneum abrasion & cecum abrasion, two knots & cecum abrasion, and four knots & cecum abrasion models, to explore the influence of double opposed wounds and foreign materials. The findings suggest that the peritoneum resection & cecum abrasion model is a reliable and reproducible PA model, as it exhibits a high adhesion rate and consistent adhesion scores across all evaluation approaches. The utilization of the double‐opposed wound model and evaluation systems contributes to more scientifically rigorous and efficient preclinical evaluations of various anti‐adhesion agents.

Estimating the canopy chlorophyll content of winter wheat under nitrogen deficiency and powdery mildew stress using machine learning

As an important indicator of the photosynthetic capacity of crops, the canopy chlorophyll content (CCC) is nondestructively estimated by reflectance using various spectrometers. Crop growth is often severely affected by Nitrogen (N) deficiency and diseases, and the compatibility of the data collected for different stressors needs further clarification to develop unified estimation models. In this field experimental study, hyperspectral data of the wheat canopy were collected, along with canopy chlorophyll content, to assess nitrogen deficiency and powdery mildew stress. Comparative analysis of hyperspectral remote sensing data input features (original reflectance (OR), spectral index (SI) and wavelet features (WF)) was conducted. A combination of feature selection and machine learning was used to determine the best estimation mode for the accurate inversion of CCC under these two stressors. The results showed that the canopy spectra under nitrogen deficiency and powdery mildew stress had the same change trend. Under nitrogen deficiency, the sensitive wavelengths to CCC mainly reflected canopy structure characteristics, followed by pigments. Under powdery mildew stress, the sensitive wavelengths mainly reflected pigment characteristics, followed by canopy structure. Eight input features (two reflectance wavelengths, two spectral indices and four wavelet features) were selected using competitive adaptive reweighted sampling (CARS) and variance inflation factor (VIF) methods. Machine learning (ML) produced better estimates for both stressors. For CCC estimation under nitrogen stress, random forest regression (RFR) was more suitable (R2 = 0.828; RMSE = 0.363 g/m2) and showed a higher accuracy for both the calibration and validation sets. For CCC estimation under powdery mildew stress, support vector machine regression (SVR) was more suitable (R2 = 0.787; RMSE = 0.126 g/m2), especially when OR and WF data were used as input features. For the unified estimation of CCC under both stressors, WF is most effective as an input feature and good accuracy is achieved for both SVR (R2 = 0.846; RMSE = 0.296 g/m2) and RFR (R2 = 0.844; RMSE = 0.297 g/m2), and their differences are very small. The results demonstrated that the CWT-CARS-VIF-ML mode was appropriate for CCC estimation under two different stressors, which provides an ideal reference and technical guidance for the evaluation of photosynthetic potential and improved crop management.

A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics.

During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process.

Higher Flavor Symmetries in the Standard Model

AbstractA study of the generalized global flavor symmetries of the Standard Model is initiated. The presence of nonzero triangle diagrams between the U(3)5 flavor currents and the hypercharge current intertwines them in the form of a higher‐group which mixes the zero‐form flavor symmetries with the one‐form magnetic hypercharge symmetry. This higher symmetry structure greatly restricts the possible flavor symmetries that may remain unbroken in any ultraviolet completion that includes magnetic monopoles. In the context of unification, this implies tight constraints on the combinations of fermion species which may be joined into multiplets. Three of four elementary possibilities are reflected in the classic unification models of Georgi–Glashow, , and Pati–Salam. The final pattern is realized non‐trivially in trinification, which exhibits the sense in which Standard Model Yukawa couplings which violate these flavor symmetries may be thought of as spurions of the higher‐group. Such modifications of the ultraviolet flavor symmetries are possible only if new vector‐like matter is introduced with masses suppressed from the unification scale by the Yukawa couplings.

Large-scale clustering of buried X-ray AGN: Trends in AGN obscuration and redshift evolution

Aims. In order to test active galactic nucleus (AGN) unification and evolutionary models, we measured the AGN clustering properties as a function of AGN obscuration defined in terms of hydrogen column density, NH. In addition to measuring the clustering of unobscured (NH < 1022 cm−2) and moderately obscured (1022 ≤ NH < 1023.5) AGNs, we also targeted highly obscured sources (NH ≥ 1023.5) up to redshifts of z = 3. Methods. We have compiled one of the largest samples of X-ray-selected AGNs from a total of eight deep XMM/Chandra and multiwavelength surveys. We measured the clustering as a function of both AGN obscuration and redshift using the projected two-point correlation function, wp(rp). We modeled the large-scale clustering signal, measured the AGN bias, b(z, NH), and interpreted it in terms of the typical AGN host dark matter halo, Mhalo(z, NH). Results. We find no significant dependence of AGN clustering on obscuration, suggesting similar typical masses of the hosting halos as a function of NH. This result matches expectations of AGN unification models, in which AGN obscuration depends mainly on the viewing angle of the obscuring torus. We measured, for the first time, the clustering of highly obscured AGNs and find that these objects reside in halos with typical mass log Mhalo = 12.98−0.22+0.17[h−1 M⊙] (12.28−0.19+0.13) at low z ∼ 0.7 (high z ∼ 1.8) redshifts. We find that irrespective of obscuration, an increase in AGN bias with redshift is slower than the expectation for a constant halo mass and instead follows the growth rate of halos, known as the passive evolution track. This implies that for those AGNs the clustering is mainly driven by the mass growth rate of the hosting halos and galaxies across cosmic time.

Unified two-component interfacial area concentration model for narrow channel two-phase flow

The conventional interfacial area concentration (IAC) models do not distinguish narrow and wide side interfaces when applied in narrow channels, which hinders accurate modeling of interfacial transfer mechanisms at different interfaces. In order to overcome such deficiency, void fraction data obtained in narrow-channel air-water flow with the flexible printed circuit (FPC) wire-mesh sensor (WMS) is analyzed to establish a two-component IAC model which consists of two essential parameters, i.e. the projected interfacial area concentration (PIAC) and projected interfacial length concentration (PILC). Based on the refined and sharpened void fraction distribution, the postprocessing algorithm is developed to obtain the PIAC and PILC for different flow patterns. Analysis of experimental data indicates that the PIAC is approximately proportional to void fraction, while variation of the PILC with void fraction is complicated. The ratio of PILC to PIAC, which characterizes topology of interfaces, shows distinct value in bubbly flow compared with other flow patterns. Such difference is attributed to the existence of large bubbles, typically with spanwise length over 70% of the channel width, in flow patterns other than bubbly flow. Hence, bubbles are separated into two groups with the critical ratio of bubble width to channel width in the model derivation. The semi-empirical PIAC and PILC models are derived for each bubble group. Based on volume fraction of two bubble groups, the unified PIAC and PILC models are obtained to realize the accurate prediction in all the flow patterns. The unified two-component IAC model and other IAC models are evaluated with the present IAC experimental data and the experimental data of Shen et al. (2012). The evaluation shows the superiority of the unified two-component IAC model and that the proposed unified two-component model can be applied in the narrow-channel air-water two-phase flow patterns with α¯≤0.9.

Time-symmetric quantization of relativistic fields. 1. Complex fields, massless gauge fields and gravitons

In the standard formulation of quantum field theory (QFT), where there are only positive energy particles and antiparticles, the energy and charge of the vacuum diverge, which, due to the existence of gravity, leads to the inconsistency of the theory (cosmological constant problem). In the article, it is shown that in the Stueckelberg-Feynman (SF) interpretation, where antiparticles are described as negative energy particles moving backward in time, the zero-point energy and zero-point charge of vacuum of complex fields are absent and there is no cosmological constant problem. However, until now it was believed that the SF interpretation leads to negative probabilities and incompatible with QFT. In the article, it is presented a new formulation of QFT on the basis of the SF interpretation in the form of time-symmetric quantization (TSQ), where the probability of states is positive. In TSQ, the consequences of the SF interpretation are taken into account consecutively and it is shown that: a) the negative sign of the norm of states only changes the sign of the wave function, and not the probabilities; b) the expression of backward in time integrals through the forward in time integrals changes sign; c) the time ordering of the operators is symmetric in time and writing them through the usual ordering leads to the standard diagram technique. For this reason, TSQ correctly describes the known observable effects. In TSQ, the results of unification models change, in particular, a) there is no zero-point energy even with broken supersymmetry between complex fields; b) there is no zero-point energy of modes in string theories, which allows to include gravity, but there is no a conformal anomaly and the dimension of space can be arbitrary.

A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities.

The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach's effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape.

Review: Inelastic Constitutive Modeling: Polycrystalline Materials.

This article provides a literature review that details the development of inelastic constitutive modeling as it relates to polycrystalline materials. This review distinguishes between inelastic constitutive models that account for nonlinear behavior at the microstructural level, time-independent classic plasticity models, and time-dependent unified models. Particular emphasis is placed on understanding the underlying theoretical framework for unified viscoplasticity models where creep and classical plasticity behavior are considered the result of applied boundary conditions instead of separable rates representing distinct physical mechanisms. This article establishes a clear understanding of the advantages of the unified approach to improve material modeling. This review also discusses recent topics in constitutive modeling that offer new techniques that bridge the gap between the microstructure and the continuum.

Multimodal Medical Image Fusion: The Perspective of Deep Learning

Multimodal medical image fusion involves the integration of medical images originating from distinct modalities and captured by various sensors, with the aim to enhance image quality, minimize redundant information, and preserve specific features, ultimately leading to increased efficiency and accuracy in clinical diagnoses. In recent years, the emergence of deep learning techniques has propelled significant advancements in image fusion, addressing the limitations of conventional methods that necessitate manual design of activity level measurement and fusion rules. This paper initially presents a systematic description of the multimodal medical image fusion problem, delineating the interrelationships between different fusion modalities while summarizing their characteristics and functions. Subsequently, it reviews the theories and enhancement approaches associated with deep learning in the medical image fusion domain, striving for a comprehensive overview of the state-of-the-art developments in this field from a deep learning perspective. These developments encompass multimodal feature extraction methods based on convolutional techniques, adversarial learning-based methods, convolutional sparse representation and stacked autoencoder-based signal processing methods, and unified models. Lastly, the paper summarizes the enhancement techniques for multimodal medical image fusion methods, highlighting the pressing issues and challenges encountered by deep learning approaches in this domain.

Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus

ObjectiveDeep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei—including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)—across different DBS frequencies. Materials and MethodsBoth synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim—across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. ResultsOur model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. ConclusionsThe result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

Machine learning-aided line intensity ratio technique applied to deuterium plasmas

It has been demonstrated that the electron density, ne, and temperature, Te, are successfully evaluated from He I line intensity ratios coupled with machine learning (ML). In this paper, the ML-aided line intensity ratio technique is applied to deuterium (D) plasmas with 0.031 < ne (1018 m−3) < 0.67 and 2.3 < Te (eV) < 5.1 in the PISCES-A linear plasma device. Two line intensity ratios, Dα/Dγ and Dα/Dβ, are used to develop a predictive model for ne and Te separately. Reasonable agreement of both ne and Te with those from single Langmuir probe measurements is obtained at ne > 0.1 × 1018 m−3. Addition of the D2/Dα intensity ratio, where the D2 band emission intensity is integrated in a wavelength range of λ ∼ 557.4–643.0 nm, is found to improve the prediction of, in particular, ne, and Te. It is also confirmed that the technique works for D plasmas with 0.067 < ne (1018 m−3) < 6.1 and 0.8 < Te (eV) < 15 in another linear plasma device, PISCES-RF. The two training datasets from PISCES-A and PISCES-RF are combined, and unified predictive models for ne and Te give reasonable agreement with probe measurements in both devices.

Comparing the host galaxy ages of X-ray selected AGN in COSMOS

We explore the properties of the host galaxies of X-ray selected AGN in the COSMOS field using the Chandra Legacy sample and the LEGA-C survey VLT optical spectra. Our main goal is to compare the relative ages of the host galaxies of the obscured and unobscured AGN by means of the calcium break Dn(4000) and the Hδ Balmer line. The host galaxy ages are examined in conjunction with other properties such as the galaxy stellar mass, and star-formation rate as well as the AGN Eddington ratio. Our sample consists of 50 unobscured or mildly obscured (NH < 1023 cm−2) and 23 heavily obscured AGN (NH > 1023 cm−2) in the redshift range z = 0.6 − 1. We take specific caution to create control samples in order to match the exact luminosity and redshift distributions for the obscured and unobscured AGN. The majority of unobscured AGN appear to live in young galaxies in contrast to the obscured AGN which appear to live in galaxies located between the young and old galaxy populations. This finding may be in contrast to those evolutionary AGN unification models which postulate that the AGN begin their life in a heavy obscuration phase. The host galaxies of the obscured AGN have significantly lower levels of specific star-formation. At the same time the obscured AGN have lower Eddington ratios indicating a link between the star-formation and the black hole accretion. We find that the distribution of the stellar masses of the host galaxies of obscured AGN is skewed towards higher stellar masses in agreement with previous findings. Our results on the relative age of obscured AGN are valid when we match our obscured and unobscured AGN samples according to the stellar mass of their host galaxies. All the above results become less conspicuous when a lower column density (log NH(cm−2) = 21.5 or 22) is used to separate the obscured and unobscured AGN populations.

Fuzzy Logic and Decision Making Applied to Customer Service Optimization

In the literature, the Information Technology Infrastructure Library (ITIL) methodology recommends determining the priority of incident resolution based on the impact and urgency of interactions. The RFID model, based on the parameters of Recency, Frequency, Importance and Duration in the resolution of incidents, provides an individual assessment and a clustering of customers based on these factors. We can improve the traditional concept of waiting queues for customer service management by using a procedure that adds to the evaluation provided by RFID such additional factors as Impact, Urgency and Emotional character of each interaction. If we also include aspects such as Waiting Time and Contact Center Workload, we have a procedure that allows prioritizing interactions between the customer and the Contact Center dynamically and in real time. In this paper we propose to apply a model of unification of heterogeneous information in 2-tuple linguistic evaluations, to obtain a global evaluation of each interaction by applying the Analytic Hierarchy Process (AHP), and in this way be able to have a dynamic process of prioritization of interactions.

Multi-epoch hard X-ray view of Compton-thick AGN Circinus Galaxy

ABSTRACT The circumnuclear material around active galactic nuclei (AGNs) is one of the essential components of the obscuration-based unification model. However, our understanding of the circumnuclear material in terms of its geometrical shape, structure, and its dependence on accretion rate is still debated. In this paper, we present the multi-epoch broad-band X-ray spectral modelling of a nearby Compton-thick AGN in Circinus galaxy. We utilize all the available hard X-ray (>10 keV) observations taken from different telescopes, i.e. BeppoSAX, Suzaku, NuSTAR, and AstroSat, at 10 different epochs across 22 yr from 1998 to 2020. The 3.0–79 keV broad-band X-ray spectral modelling using physically motivated models, namely MYTorus, borus02, and uxclumpy, infers the presence of a torus with a low covering factor of 0.28, an inclination angle of 77°–81° and Compton-thick line-of-sight column densities (NH,LOS = 4.13–9.26 × 1024 cm−2) in all the epochs. The joint multi-epoch spectral modelling suggests that the overall structure of the torus is likely to remain unchanged. However, we find tentative evidence for the variable line-of-sight column density on time-scales ranging from 1 d to 1 week to a few years, suggesting a clumpy circumnuclear material located at subparsec to tens of parsec scales.

SU(6) gauge-Higgs grand unification: minimal viable models and flavor

Gauge-Higgs grand unification theories are models of gauge-Higgs unification that extend the electroweak group into a simple group that includes the color symmetry. The minimal option is a gauge-Higgs grand unification based on the SU(6) gauge group, mirroring SU(5) grand unification in 4D while providing a solution to the hierarchy problem. We explore different minimal and realistic novel incarnations of SU(6) gauge-Higgs grand unification. We submit the setup to the various flavor hierarchies observed in nature and, utilizing the power of the fifth dimension, identify an embedding that provides a compelling model of quarks and leptons that naturally explains the mass hierarchies and the CKM/PMNS structure. We perform a detailed study of quark- and lepton-flavor constraints (which are intimately related due to the GUT nature) together with an analysis of the Higgs potential which arises at the loop level. Electroweak precision constraints on the model are discussed and the rich scalar sector is analyzed. Future flavor constraints from upcoming experiments will provide a stringent test for this class of models, while a scalar singlet and leptoquark provide unique targets for current and future collider experiments to probe this solution to various open questions in nature.