Degrees Of Irregularity Research Articles

A Localization Algorithm Based on Improved Water Flow Optimizer and Max-Similarity Path for 3-D Heterogeneous Wireless Sensor Networks

Localization is one of the key technologies in wireless sensor networks (WSNs) because the information measured by the sensors is meaningful only if the location of the sensor nodes is known. The existing localization algorithms have low localization accuracy in 3-D heterogeneous WSNs (HWSNs) and cannot meet the user’s needs well. In this article, a localization algorithm based on an improved water flow optimizer and improved max-similarity path (IWFO-IMSP) is proposed. First, based on the shortest communication path among nodes in the network, the maximum similarity path is calculated. Second, the distance between the unknown node and the anchor node is estimated using the distance of the maximum similarity path minus the distance of the dissimilar part. Third, the cosine theorem is introduced to correct the polyline distance of the path to a straight-line distance to further improve the accuracy of the distance estimation. Finally, the convergence speed and convergence ability of the water flow optimizer (WFO) are improved by fusing various strategies such as the Halton sequence and Cauchy mutation. The location of the unknown node is searched out by using the improved WFO (IWFO) to achieve accurate localization. By comparing and analyzing the simulation 3-D space and 3-D C-space with different degrees of irregularity (DOI), the localization error of IWFO-IMSP is reduced by about 70%, 80%, 12%, and 45% relative to improved DV-Hop (IDV-Hop), projection correction of closer points (CPPA), similar path-based localization algorithm (NLA-SP), and DV-maxhop, respectively. Therefore, the localization accuracy of IWFO-IMSP is much higher than the other four localization algorithms.

Evaluation of Haematological and Immune Status of Patients with Renal Dysfunction in Haematology and Blood Transfusion Department in a Tertiary Health Institution

Background: Renal dysfunction is a global public health issue where steadily progressive degradation of kidney function leads to multiple haematological and immune dysfunctions that, if adequate steps are not taken to monitor them, it renders patients vulnerable to cardiovascular morbidity and mortality. This study was aimed at evaluating the haematological and immune status of patients with renal dysfunction in blood and transfusion department of the National Hospital Abuja, Nigeria. Methods: Eighty-eight participants comprising 60 renal failure patients and 28 healthy control subjects were recruited into the study between January and August 2020. Haemoglobin parameters such as total white blood cell count (WBC), haemoglobin (Hb), packed cell volume (PCV) and platelet (PLT) counts were assessed for the subjects and controls. Similarly, CD4 and CD8 counts were also determined for subjects and controls. Results were analyzed using IBM SPSS Inc., Chicago, IL, USA version 25.0 for Windows. Results: PCV, Hb, and PLTs significantly reduced in subjects (24.0815.7%, 7.9711.76 g/dl, and 150.06184.18 x109/L) compared to the control (41.312.52%, 13.7610.74 g/dl, and 252.6159.34 x109/L) whereas WBC significantly increased in case (5.7912.99 x109/L) than control (5.1811.29 x109/L), P < 0.05. The CD4 and CD8 count significantly decreased (P<0.001) in patients with renal failure (180.22169.49 cells/µL and 145.76150.87 cells/pl) compared to control (925182.62 cells/µL and 809.8195.02 cells/pl) (P<0.05). There was a significant difference (P<0.001) between anaemia grades for patients with renal failure and control. Conclusions: Renal dysfunction is significantly associated with various degrees of irregularity in haematological parameters that need proper evaluation, monitoring and treatment.

Multifractal test for nonlinearity of interactions across scales in time series.

The creativity and emergence of biological and psychological behavior tend to be nonlinear, and correspondingly, biological and psychological measures contain degrees of irregularity. The linear model might fail to reduce these measurements to a sum of independent random factors (yielding a stable mean for the measurement), implying nonlinear changes over time. The present work reviews some of the concepts implicated in nonlinear changes over time and details the mathematical steps involved in their identification. It introduces multifractality as a mathematical framework helpful in determining whether and to what degree the measured series exhibits nonlinear changes over time. These mathematical steps include multifractal analysis and surrogate data production for resolving when multifractality entails nonlinear changes over time. Ultimately, when measurements fail to fit the structures of the traditional linear model, multifractal modeling allows for making those nonlinear excursions explicit, that is, to come up with a quantitative estimate of how strongly events may interact across timescales. This estimate may serve some interests as merely a potentially statistically significant indicator of independence failing to hold, but we suspect that this estimate might serve more generally as a predictor of perceptuomotor or cognitive performance.

Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs

Schizophrenia subjects often suffer from a failure to properly predict incoming inputs; most notably, some patients exhibit impaired prediction of the sensory consequences of their own actions. The mechanisms underlying this deficit remain unclear, though. One possible mechanism could consist in aberrant predictive processing, as schizophrenic patients show relatively less attenuated neuronal activity to self-produced tones, than healthy controls. Here, we tested the hypothesis that this aberrant predictive mechanism would manifest itself in the temporal irregularity of neuronal signals. For that purpose, we here introduce an event-related potential (ERP) study model analysis that consists of an EEG real-time model equation, eeg(t) and a frequency Laplace transformed Transfer Function (TF) equation, eeg(s). Combining circuit analysis with control and cable theory, we estimate the temporal model representations of auditory ERPs to reveal neural mechanisms that make predictions about self-generated sensations. We use data from 49 schizophrenic patients (SZ) and 32 healthy control (HC) subjects in an auditory ‘prediction’ paradigm; i.e., who either pressed a button to deliver a sound tone (epoch a), or just heard the tone without button press (epoch b). Our results show significantly higher degrees of temporal irregularity or imprecision between different trials of the ERP from the Cz electrode (N100, P200) in SZ compared to HC (Levene’s test, p < 0.0001) as indexed by altered latency, lower similarity/correlation of single trial time courses (using dynamic time warping), and longer settling times to reach steady state in the intertrial interval. Using machine learning, SZ vs HC could be highly accurately classified (92%) based on the temporal parameters of their ERPs’ TF models, using as features the poles of the TF rational functions. Together, our findings show temporal irregularity or imprecision between single trials to be abnormally increased in SZ. This may indicate a general impairment of SZ, related to precisely predicting the sensory consequences of one’s actions.

Equivalent frame discretisation for URM fa\xe7ades with irregular opening layouts

Researchers and practitioners widely employ simplified Equivalent Frame Models (EFM) for reproducing the in-plane governed response of unreinforced brick masonry (URM) structures, as they typically represent an acceptable compromise between numerical accuracy and computational cost. However, when considering URM structural systems with irregular opening distribution, the definition of the effective height and length of deformable components (i.e. pier and spandrel elements) still represents an open challenge. In this work, the influence of irregular distribution of openings on the predicted lateral response of full-scale URM façades was investigated. To this end, several geometrical combinations characterised by various degrees of irregularity were considered and idealised according to commonly employed EF discretisation approaches. Then, after a preliminary calibration process against experimental tests on both individual piers and a full-scale building façade, EFM results were compared with micro-modelling predictions, carried out within the framework of the Applied Element Method and used as a benchmark. Although in specific irregular configurations using some discretisation approaches, macro and micro-models converge to similar results, non-negligible differences in terms of initial lateral stiffness, base-shear and damage distribution were observed with other EF schemes or opening layouts, thus indicating that a careful selection of appropriate criteria is indeed needed when performing in-plane analyses of URM systems with irregular opening distributions. Finally, building on inferred simulated data, potential solutions are given to overcome typical EF discretisation issues and better approximate micro-modelling outcomes.

Validation of a 4D-MRI guided liver stereotactic body radiation therapy strategy for implementation on the MR-linac

Purpose. Accurate tumor localization for image-guided liver stereotactic body radiation therapy (SBRT) is challenging due to respiratory motion and poor tumor visibility on conventional x-ray based images. Novel integrated MRI and radiotherapy systems enable direct in-room tumor visualization, potentially increasing treatment accuracy. As these systems currently do not provide a 4D image-guided radiotherapy strategy, we developed a 4D-MRI guided liver SBRT workflow and validated all steps for implementation on the Unity MR-linac. Materials and Methods. The proposed workflow consists of five steps: (1) acquisition of a daily 4D-MRI scan, (2) 4D-MRI to mid-position planning-CT rigid tumor registration, (3) calculation of daily tumor midP misalignment, (4) plan adaptation using adapt-to-position (ATP) with segment-weights optimization and (5) adapted plan delivery. The workflow was first validated in a motion phantom, performing regular motion at different baselines (±5 to ±10 mm) and patient-derived respiratory signals with varying degrees of irregularity. 4D-MRI derived respiratory signals and 4D-MRI to planning CT registrations were compared to the phantom input, and gamma and dose-area-histogram analyses were performed on the delivered dose distributions on film. Additionally, 4D-MRI to CT registration performance was evaluated in patient images using the full-circle method (transitivity analysis). Plan adaption was further analyzed in-silico by creating adapted treatment plans for 15 patients with oligometastatic liver disease. Results. Phantom trajectories could be reliably extracted from 4D-MRI scans and 4D-MRI to CT registration showed submillimeter accuracy. The DAH-analysis demonstrated excellent coverage of the dose evaluation structures GTV and GTVTD. The median daily rigid 4D-MRI to midP-CT registration precision in patient images was <2 mm. The ATP strategy restored the target dose without increased exposure to the OARs and plan quality was independent from 3D shift distance in the range of 1–26 mm. Conclusions. The proposed 4D-MRI guided strategy showed excellent performance in all workflow tests in preparation of the clinical introduction on the Unity MR-linac.

Quantitative assessment of seismic design provisions for buildings with torsional irregularities

ASCE/SEI 7 design requirements for seismic-induced torsion in buildings are evaluated to determine their effectiveness for resisting seismic-induced collapse of torsionally irregular buildings. The ASCE/SEI 7-16 provisions are found to be generally conservative for most torsionally irregular building configurations—exceptions are some buildings that rely heavily on lines of lateral resistance orthogonal to the design earthquake force to resist torsional moments, and also some torsionally flexible buildings designed using modal response spectrum analysis. Modifications to provide better consistency in collapse resistance over a large range of building configurations and degrees of torsional irregularity are recommended. The study also demonstrates that buildings classified as extremely torsionally irregular may not need to be prohibited from Seismic Design Categories E and F, as long as the lateral system is proportioned properly.

Anisotropic functional deconvolution for the irregular design: A minimax study

Anisotropic functional deconvolution model is investigated in the bivariate case when the design points ti , and xl , are irregular and follow known densities h 1, h 2, respectively. In particular, we focus on the case when the densities h 1 and h 2 have singularities, but and are still integrable on [0, 1]. We construct an adaptive wavelet estimator that attains asymptotically near-optimal convergence rates in a wide range of Besov balls. The convergence rates are completely new and depend on a balance between the smoothness and the spatial homogeneity of the unknown function f, the degree of ill-posed-ness of the convolution operator and the degrees of spatial irregularity associated with h 1 and h 2. Nevertheless, the spatial irregularity affects convergence rates only when f is spatially inhomogeneous in either direction.

The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets.

We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to estimate degrees of stability and irregularity in cryptocurrency and international stock markets. The amount of regularity infers on the unpredictability of fluctuations. The t-test and F-test are performed on estimated LLE and ApEn. In total, 36 statistical tests are performed to check for differences between time periods (pre- versus during COVID-19 pandemic samples) on the one hand, as well as check for differences between markets (cryptocurrencies versus stocks), on the other hand. During the COVID-19 pandemic period it was found that (a) the level of stability in cryptocurrency markets has significantly diminished while the irregularity level significantly augmented, (b) the level of stability in international equity markets has not changed but gained more irregularity, (c) cryptocurrencies became more volatile, (d) the variability in stability and irregularity in equities has not been affected, (e) cryptocurrency and stock markets exhibit a similar degree of stability in price dynamics, whilst finally (f) cryptocurrency exhibit a low level of regularity compared to international equity markets. We find that cryptos showed more instability and more irregularity during the COVID-19 pandemic compared to international stock markets. Thus, from an informational efficiency perspective, investing in digital assets during big crises as the COVID-19 pandemic, could be considered riskier as opposed to equities.

Seismic vulnerability through drift assessment for bridges with geometrical irregularities

In this work, the analyses of common types of highway bridges under seismic loading are presented for several geometrical irregularity conditions on either the substructure or superstructure. In the first series of analyses, the influence of the substructure is studied by varying the central pier height, and the height of the pier next to the support, to compare the corresponding responses at deck displacement level. In the second series of analyses, the superstructure irregularity is examined trough variations on the extreme span length, as compared to the length of the central span, and for curved bridges; both conditions are studied for several degrees of irregularity. All the bridges are subjected to 53 accelerograms recorded on the highest seismic hazard zone in Mexico. From these analyses, an index defined by the normalized difference on the maximum displacements between the regular and the irregular systems is calculated. In addition, fragility curves with the normalized differences for each irregularity condition are developed. The information provided by the index is complemented with these curves and the index behavior and its dispersion are drawn in a global way. The proposed index does not follow a linear trend to describe the bridge seismic vulnerability, as considered in the current Mexican practice. The obtained results are used to propose fragility categories, and their associated relative ponderation weights, to assess the substructure and superstructure bridge irregularity and to identify potential aspects were the bridge inspector may focus on in order to make more detailed work to pursue maintenance decisions.

Inferring neural circuit structure from datasets of heterogeneous tuning curves.

Tuning curves characterizing the response selectivities of biological neurons can exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or partially random connectivity also give rise to diverse tuning curves. Empirical tuning curve distributions can thus be utilized to make model-based inferences about the statistics of single-cell parameters and network connectivity. However, a general framework for such an inference or fitting procedure is lacking. We address this problem by proposing to view mechanistic network models as implicit generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable. Recent advances in machine learning provide ways for fitting implicit generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GANs) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic circuit models in theoretical neuroscience to datasets of tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, such as the biophysical parameters of, or synaptic connections between, particular recorded cells. Instead, it directly learns generalizable model parameters characterizing the network’s statistical structure such as the statistics of strength and spatial range of connections between different cell types. Another strength of this approach is that it fits the joint high-dimensional distribution of tuning curves, instead of matching a few summary statistics picked a priori by the user, resulting in a more accurate inference of circuit properties. More generally, this framework opens the door to direct model-based inference of circuit structure from data beyond single-cell tuning curves, such as simultaneous population recordings.

Range-free localization algorithm using a customary drone: Towards a realistic scenario

The localization of devices is a key ingredient of Internet of Things (IoT) which may requireextra cost for deploying anchor nodes aware of their position. In this work, we propose a range-free localization algorithm, called Drone Range-Free (briefly, Drf), that replaces the anchor nodes with an off-the-shelf drone. When the radio communications are isotropic, our algorithm localizes the IoT devices ensuring the level of precision required by the final user. However, since an isotropic antenna that propagates its signal in a perfect sphere does not exist in reality, we face a more realistic scenario by proposing an enhanced version of our localization algorithm, called Drone Range-Free Enhanced (briefly, DrfE), that well tolerates high degrees of irregularity of the antenna signal. Under the anisotropic model, Drf and DrfE exceed in precision all the computational-light previous known range-free localization algorithms based on a mobile rover.

Influence of fiber spatial distribution in unidirectional composite cross-section on homogenized elastic parameters

The aim of this work is to analyze the influence of spatial distribution of fibers in unidirectional longfiber composite material on the mechanical behavior of a lamina using finite element models of unit cells (micromodels). The dependencies of effective elastic parameters of a lamina on degree of irregularity of spatial fiber distribution are investigated, proved and described.Degree of irregularity of real composite material cross-sections and the unit cells is described by a proposed parameter. This parameter expresses deviation of a given composite cross-section (2D geometry) from idealized regular hexagonal fiber distribution.Effective elastic parameters of 200 micromodels with geometries having different degrees of irregularity are calculated. A dependence between the parameter evaluating the cross-section irregularity and the spread (interval between extreme values) of the resulting effective parameters is obtained.

Constructing Highly Uniform Onion-Ring-like Graphitic Carbon Nitride for Efficient Visible-Light-Driven Photocatalytic Hydrogen Evolution.

The introduction of microstructure to the metal-free graphitic carbon nitride (g-C3N4) photocatalyst holds promise in enhancing its catalytic performance. However, producing such microstructured g-C3N4 remains technically challenging due to a complicated synthetic process and high cost. In this study, we develop a facile and in-air chemical vapor deposition (CVD) method that produces onion-ring-like g-C3N4 microstructures in a simple, reliable, and economical manner. This method involves the use of randomly packed 350 nm SiO2 microspheres as a hard template and melamine as a CVD precursor for the deposition of a thin layer of g-C3N4 in the narrow space between the SiO2 microspheres. After dissolution of the microsphere template, the resultant g-C3N4 exhibits uniquely uniform onion-ring-like microstructures. Unlike previously reported g-C3N4 powder morphologies that show various degrees of agglomeration and irregularity, the onion-ring-like g-C3N4 is highly dispersed and uniform. The calculated band gap for onion-ring-like g-C3N4 is 2.58 eV, which is significantly narrower than that of bulk g-C3N4 at 2.70 eV. Experimental characterization and testing suggest that, in comparison with bulk g-C3N4, onion-ring-like g-C3N4 facilitates charge separation, extends the lifetime of photoinduced carriers, exhibits 5-fold higher photocatalytic hydrogen evolution, and shows great potential for photocatalytic applications.

Quantitative effect of Ar dilution on the dynamic detonation parameters in acetylene–oxygen mixtures

In order to clarify the effect of Ar dilution on the dynamic parameters of detonation quantitative, smoked foil records of C2H2–O2 with different Ar dilutions are investigated. With the Ar concentration increasing, the instability will decrease and detonation can fail more easily. Histograms, variance measurements, and studies of the autocorrelation function are systematically considered to quantitative define the irregularity parameters. The other kinetics characteristics, such as detonation limits, velocity, and cell size, are also analyzed to illustrate the dilution effects via the smoke foils from different Ar dilution. It was found that the results from histograms, variance studies, and the autocorrelation function consistently show the tendency that mixtures with different Ar dilutions exhibited different degrees of irregularity. Quantitative irregularity can be examined by the values of the ratio Max discrepancy/Relevant mean cell size of C2H2 + 2.5O2. For the mixtures with higher Ar concentration, the main cell size shown by the peak in the histogram and the first peak of the autocorrelation function were much closer to the cell size measured visually because of the stronger dominant mode and more regular trajectory of triple-point patterns. Predictions of the cell size considering the participation of Ar in the mixtures were also given.

Characterizing constraining forces in the alignment phase of orthodontic treatment.

To describe the frictional forces (FF) that constrain wire sliding in the initial alignment phase of treatment using a new term, the "constraining force" (CF), and to hypothesize that CF is dependent on two factors: the hyperelastic behavior of archwires and the specific type of tooth geometric malalignment present. A laboratory device that simulates the four distinct malalignment types (in-out, rotation, tipping, and vertical step) was used to couple with an Instron testing apparatus. Incremental CF data for the four types of malalignment were recorded. Each type had five trials per increment of severity, from which the CF was averaged using 0.016-inch copper-nickel-titanium (CuNiTi) archwires. Two types of friction curves were obtained: a traditional step function response and a power regression response. For all malalignment types, increasing degrees of irregularity increased power regression responses and CF. A severity turning point, displayed as a sudden increase in CF, occurred for each malalignment. The rotation type of malalignment yielded the lowest CF, while the vertical step type resulted in the highest CF. The data infer a hypothesis that malrotation type having weak CF might act as a limiting factor in the alignment phase to unravel the neighboring teeth. Future investigations to compare clinical and bench data can help explain more fully the constraints impeding alignment resolution and the factors governing the ability to bring malaligned teeth into alignment.

Numerical Study of the Shape Irregularity Gradient in Metallic Foams Under Different Impact Velocities

The properties of metallic foams are closely related to the average pore size, relative density and degree of the shape irregularity in the meso-structure. In this paper, gradient metallic foams with Voronoi structures based on different degrees of shape irregularity are constructed and numerically tested under different impact velocities. We combine three types of metallic foams with identical relative density but different degrees of shape irregularity in a specific sequence (from large to small or from small to large) to construct the new gradient models. A series of impact tests at different velocities are performed on the gradient metallic foams to acquire deformation characteristics and stress states. According to the results from the inertia effect and the energy absorption capacity, a gradient metallic foam with a shape irregularity that changes from large to small (negative gradient) is the optimal design. The simulation results provide a fresh perspective and methodology to design gradient metallic foams at the meso-structure scale for use as energy absorption materials.

Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns

The capacity of functional MRI (fMRI) to resolve cortical columns depends on several factors. These include the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the signal-to-noise ratio (SNR) considering thermal and physiological noise. However, it remains unknown how these factors combine, and what is the voxel size that optimizes fMRI of cortical columns.Here we combine current knowledge into a quantitative model of fMRI of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We compare different approaches for identifying patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and high-resolution imaging and reconstruction of the pattern of cortical columns. We present the dependence of the performance of each approach on the parameters of the imaged pattern as well as those of the data acquisition. In addition, we predict voxel sizes that optimize fMRI of cortical columns under various scenarios.We found that all measures associated with multivariate detection and decoding could be approximately calculated from a measure we termed “multivariate contrast-to-noise ratio” (mv-CNR), which is a function of the contrast-to-noise ratio (CNR) and number of voxels. Furthermore, mv-CNR implied that the optimal voxel width for detection and decoding is independent of changes in response amplitude, SNR and imaged volume that are not caused by changes in voxel size.For regular patterns, optimal voxel widths for detection, decoding and imaging/reconstructing the pattern of cortical columns were approximately half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T, and Spin-Echo fMRI at 7T on the detection, decoding, and reconstruction measures considered and found that in all cases the width of the fMRI point-spread had the most significant effect. In contrast, different response amplitudes and noise characteristics played a relatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for high-resolution imaging of cortical columns under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of high-resolution fMRI of cortical columns and the decoding of information conveyed by these columns.

Seismic collapse performance of special moment steel frames with torsional irregularities

Many building structures exhibit torsional irregularity, which is a type of horizontal irregularity. It is difficult to estimate the inelastic response of torsionally irregular structures subjected to earthquake ground motions using numerical analyses because torsionally irregular structures experience both lateral displacement and floor rotation. This study evaluates the collapse performance of multi-story model structures with various degrees of torsional irregularity via nonlinear response history analyses. This study also proposes a procedure for computing design story drift demands, which allows torsionally irregular structures to have uniform collapse risk irrespective of the degree of torsional irregularity.

Integration against the state: Irregular migrants’ agency between deportation and regularisation in the United Kingdom

Reducing the number of foreigners residing unlawfully within the borders of a state requires either their removal or the legalisation of their presence within the territory. Increasingly, governments also employ measures of internal control and limit irregular migrants’ access to rights and services in order to encourage them to leave autonomously. This article aims to contribute to current debates on how to conceptualise and account for the agency that irregular migrants themselves exercise in such contexts. Within critical migration and citizenship studies, many of their everyday actions have been described as ‘acts of citizenship’ but also as instances of ‘becoming imperceptible’, neither of which captures the whole range of strategies irregular migrants employ to strengthen their fragile position vis-à-vis the state. I argue that conceptualising their agency in terms of (self-)integration allows us to account for both: practices through which they actively become political subjects as well as those that precisely constitute a deliberate refusal to do so. Empirically, this is underpinned by an analysis of recent policy developments in the United Kingdom and a series of semi-structured interviews I conducted during 8 months of fieldwork in London with migrants experiencing different kinds and degrees of irregularity.