Intrinsic Dependency Research Articles

Defining estimand for the win ratio: Separate the true effect from censoring.

The win ratio has been increasingly used in trials with hierarchical composite endpoints. While the outcomes involved and the rule for their comparisons vary with the application, there is invariably little attention to the estimand of the resulting statistic, causing difficulties in interpretation and cross-trial comparison. We make the case for articulating the estimand as a first step to win ratio analysis and establish that the root cause for its elusiveness is its intrinsic dependency on the time frame of comparison, which, if left unspecified, is set haphazardly by trial-specific censoring. From the statistical literature, we summarize two general approaches to overcome this uncertainty-a nonparametric one that pre-specifies the time frame for all comparisons, and a semiparametric one that posits a constant win ratio across all times-each with publicly available software and real examples. Finally, we discuss unsolved challenges, such as estimand construction and inference in the presence of intercurrent events.

On the temperature sensitivity of near-surface seismic wave speeds: application to the Groningen region, the Netherlands

SUMMARY Subsurface temperature measurements play a crucial role, for instance, in optimizing geothermal power plants and monitoring heat-storage systems. Previous studies have demonstrated that time-lapse variations in temperature can be correlated with variations in seismic wave speeds, offering the potential for temperature monitoring via seismic surveys. However, an apparent discrepancy has emerged between field and laboratory experiments. Field studies predominantly report positive correlations between temperature and seismic wave speeds, while laboratory experiments often show anticorrelations. This inconsistency underscores the need for a more comprehensive, physics-based understanding of temperature-induced wave speed changes. In this study, we strive to bridge the gap between field and laboratory findings by examining several mechanisms governing temperature-induced seismic wave speed changes, namely the intrinsic temperature dependency of elastic parameters and thermally induced elasticity. We present a physics-based modelling approach to identify the primary mechanisms responsible for temperature-induced seismic wave speed changes. By considering several end-member models, we find that intrinsic temperature dependency of elastic parameters (negative correlation) compete with thermal pressure effects (positive correlation). The precise initial and boundary conditions and physical parameters of the system under consideration will determine the weight of both effects. Temperature-related dilatation does not seem to play an important role. We apply our approach to loosely consolidated sediments in the shallow subsurface of the Groningen region, where subsurface temperature fluctuations are driven by seasonal atmospheric temperature fluctuations roughly between −5 and $30\, ^\circ$C. For these models, we predict seasonal temperature-induced changes in body-wave speeds of up to 8 per cent in the first few metres of the subsurface, high-frequency (above 2 Hz) surface wave phase velocity variations in the range of 1–2 per cent, and relative changes in site amplification on the order of 4 per cent. These findings contribute to a more comprehensive understanding of the intricate relationship between temperature and near-surface seismic properties, offering insights for applications as subsurface temperature monitoring systems.

Solidarity in the EU: What Is In A Name?

The jurisprudence on EU solidarity is rapidly expanding. Notably, the Court of Justice of the EU has progressively recognized the principle of solidarity in its rulings, elevating it to constitutional status. However, as the legal scope of solidarity widens, its scope of application and meaning become increasingly complex. This article seeks to refine our understanding of solidarity as a constitutional principle of EU law. Focused on the Court’s case law, the article maps the evolution of solidarity as a constitutional principle and unpacks the ways in which solidarity has given rise to novel interpretations and extended its sphere of influence. The article concludes that while the meaning and arguably the scope of the principle have expanded, such evolution comes with its set of challenges. The intrinsic dependency of solidarity on specific relational and situational contexts raises a significant obstacle for its conceptualization as a fundamental principle underpinning the EU legal order, particularly if it has legal implications. Consequently, the article argues that elevating solidarity to a fundamental principle of EU law - one that permeates the entire legal structure of the EU and has legal consequences - would be premature until the practical manifestation of solidarity matures beyond the confines of inter-Member-State relations. The article suggests that solidarity might be more accurately conceptualized as a fundamental value rather than an all-encompassing constitutional principle of EU law.

Characterization and Selection of Probability Density Function in a Discrete Random Switching Period SVPWM Strategy

Aiming at suppressing the switching frequency harmonics in the integrated permanent magnet synchronous motor (PMSM) systems, a discrete random switching period (DRSP) space vector pulse width modulation (SVPWM) technique is proposed in the present study. In the proposed DRSP-SVPWM technique, the switching period is randomized according to a predefined frequency sequence. Accordingly, the narrow-band harmonics of the switching frequency are extended to a wider range. Then, a mathematical correlation between the probability density function and the harmonic distribution is established. Moreover, the effects of discrete uniform and beta distributions on the harmonic spreading are studied. Based on the proposed DRSP-SVPWM technique, the mathematical expression of the harmonic power spectrum is derived, demonstrating the intrinsic dependency between the discrete random factor and the harmonic energy distribution. Finally, a series of simulations and experimental results are compared to evaluate the performance of the proposed technique. The present study is expected to provide a theoretical basis for the design and selection of discrete frequency sequences. Meanwhile, it provides a reference to investigate the random SVPWM and improve noise and electromagnetic interference in power converters.

Nonlinear dynamic stability analysis of axial impact loaded structures via the nonlocal strain gradient theory

In engineering applications, there has been an overwhelming tendency towards portability, miniaturization, and integration in recent years. To link the intrinsic size dependency feature of the small-scale structure with its structural stability, the nonlocal strain gradient theory, which captures the size effect in a more general size-dependent continuum-based model, is introduced to explore the nonlinear dynamic stability behaviour of nanoplates. Four types of axial impact loading configurations, namely, sinusoidal, exponential, rectangular, and damping, are considered. Some practical factors, such as Winkler-Pasternak elastic foundation and damping, are taken into account in the analysis. The equations of motion for the size-dependent initially imperfect plate are derived in the framework of the first-order shear deformation plate theory in conjunction with the Von Kármán nonlinear terms. Then the Airy stress function corresponding to simply supported nanoplate is introduced; then, by applying the Galerkin method, the obtained differential equations are addressed by the fourth-order Runge-Kutta algorithm. Subsequently, the specific value of the critical dynamic buckling load is determined by the Volmir criterion. Organic solar cells (OSCs), a type of emerging solar-to-electrical energy conversion nanodevice, are used as an illustrative example within the existing framework. The effects of size dependency in conjunction with the pulse load configuration, the initial imperfection, the elastic foundation, as well as the damping ratio on the nonlinear dynamic buckling behaviour of the OSC are thoroughly investigated.

Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, hence significantly reducing the service latency and network traffic. In edge computing, many applications are composed of dependent tasks where the outputs of some are the inputs of others. How to offload these tasks to the network edge is a vital and challenging problem which aims to determine the placement of each running task in order to maximize the Quality-of-Service (QoS). Most of the existing studies either design heuristic algorithms that lack strong adaptivity or learning-based methods but without considering the intrinsic task dependency. Different from the existing work, we propose an intelligent task offloading scheme leveraging off-policy reinforcement learning empowered by a Sequence-to-Sequence (S2S) neural network, where the dependent tasks are represented by a Directed Acyclic Graph (DAG). To improve the training efficiency, we combine a specific off-policy policy gradient algorithm with a clipped surrogate objective. We then conduct extensive simulation experiments using heterogeneous applications modelled by synthetic DAGs. The results demonstrate that: 1) our method converges fast and steadily in training; 2) it outperforms the existing methods and approximates the optimal solution in latency and energy consumption under various scenarios.

Robust and Decoupled Position and Stiffness Control for Electrically-Driven Articulated Soft Robots

The control of articulated soft robots, i.e. robots with flexible joints and rigid links, presents a challenge due to their intrinsic elastic elements and nonlinear force-deflection dependency. This letter first proposes a discrete-time delayed unknown input-state observer based on a nominal robot model that reconstructs the total torque disturbance vector, resulting from the imperfect knowledge of the elastic torque characteristic, external torques, and other model uncertainties. Then, it introduces a robust controller, that actively compensates for the estimated uncertainty and allows bounded stability for the tracking of independent link position and joint stiffness reference signals. The convergence of the disturbance estimator and the overall system’s stability in closed loop is proven analytically, while the effectiveness of the proposed control design is first evaluated in simulations with respect to large uncertainty conditions, and then demonstrated through experiments on a real multi-degree-of-freedom articulated soft robot.

Single-step etched two-dimensional polarization splitting dual-band grating coupler for wavelength (de)multiplexing.

Diffractive periodic-structure-based grating couplers (GCs) are the most widely used devices for light coupling between optical fibers and integrated photonic devices. However, conventional GCs have limited wavelength operation and are polarization specific, which is due to the intrinsic radiation angle dependency on both wavelength and polarization. Here we propose and experimentally demonstrate a polarization-splitting dual-band grating coupler (PS-DBGC) for polarization diversity and wavelength division (de)multiplexing (WDM) operation. The four-port two-dimensional PS-DBGC is based on a periodically arranged structure with square holes, and requires only a single etch step in a 340-nm silicon-on-insulator platform. The simulation predicts that the maximum coupling efficiency (CE) of the proposed PS-DBGC is -2.8 dB and -4.6 dB for the O- and C-band, respectively. The measured peak CEs of the fabricated device are -4.7 dB at 1280 nm and -8.4 dB at 1522 nm. We anticipate that this PS-DBGC could potentially improve the performance of any future integrated WDM passive optical network.

Enhanced production of alkane hydroxylase from Penicillium chrysogenum SNP5 (MTCC13144) through feed-forward neural network and genetic algorithm

Alkane hydroxylase (AlkB), a membrane-bound enzyme has high industrial demand; however, its economical production remains challenging due to its intrinsic nature and co-factor dependency. In the current study, various critical process parameters for optimum production of AlkB have been optimized through feed forward neural network (FFNN) and genetic algorithm (GA) models using Penicillium chrysogenum SNP5 (MTCC13144). AlkB specific activity under preliminary un-optimized conditions i.e., 1% hexadecane, 7.4 pH, 11 days incubation time, 28 °C incubation temperature and 1 ml of inoculum size was 100 U/mg. ‘One variable at a time’ (OVAT) strategy was used to identify optimum physicochemical parameters and then its output data was fed to develop a model of FFNN with ‘6-12-1’ topology. Outputs of FFNN were further optimized through GA to minimize errors and intensify search level. This has provided superior predictive performances with 0.053 U/mg overall mean absolute percentage error (MAPE), 6.801 U/mg root mean square errors (RMSE), and 0.987 overall correlation coefficient (R). The AlkB specific activity improved by 3.5-fold, i.e., from 100 U/mg under preliminary un-optimized conditions to 351.32 U/mg under optimum physicochemical conditions obtained through FFNN-GA hybrid method, i.e., hexadecane (carbon source): 1.56% v/v, FeSO4: 0.63 mM, incubation temperature: 27.40 °C, pH: 7.38, incubation time: 12.35 days and inoculums size: 1.33 ml. The developed process would be a stepping stone to fulfill the high industrial demands of Alkane hydroxylase.

Drag bit/rock interface laws for the transition between two layers

This paper extends bit/rock interface laws for drag (PDC) bits, originally formulated for homogeneous rocks, to the transition between two rock layers with distinct mechanical properties. It formulates a set of relations between the weight-on-bit, the torque-on-bit, the depth-of-cut per bit revolution, and the engagement parameter of the bit in the lower rock layer. This model enables us to extend the 2D E−S diagram for the homogeneous case to a 3D E−S diagram for the transitional case, where the third dimension is related to the engagement parameter. Moreover, this model is used to derive an expression for the drilling efficiency for the transitional phase. Examples are provided for describing the 3D E−S diagram and drilling efficiency under the condition of quasi-stationary drilling (i.e., constant angular velocity, constant weight-on-bit). These examples show that the drilling efficiency depends nonlinearly on the bit engagement between the two rock layers. This intrinsic dependency is closely related to the bit shape.

Dual-View 3D Reconstruction via Learning Correspondence and Dependency of Point Cloud Regions.

Multi-view 3D reconstruction generally adopts the feature fusion strategy to guide the generation of 3D shape for objects with different views. Empirically, the correspondence learning of object regions across different views enables better feature fusion. However, such idea has not been fully exploited in existing methods. Furthermore, current methods fail to explore the intrinsic dependency among regions within a 3D shape, leading to a rough reconstruction result. To address the above issues, we propose a Dual-View 3D Point Cloud reconstruction architecture named DVPC, which takes two views images as inputs, and progressively generates a refined 3D point cloud. First, a point cloud generation network is assigned to generate a coarse point cloud for each input view. Second, a dual-view point clouds synthesis network is presented in DVPC. It constructs a regional attention mechanism to learn a high-quality correspondence among regions across two coarse point clouds in different views, so that our DVPC can achieve feature fusion accurately. And then it develops a point cloud deformation module to produce a relatively-precise point cloud via establishing the communication between the coarse point cloud and the fused feature. Lastly, a point-region transformer network is devised to model the dependency among regions within the relatively-precise point cloud. With the dependency, the relatively-precise point cloud is refined into a desirable 3D point cloud with rich details. Qualitative and quantitative experiments on the ShapeNet and Pix3D datasets demonstrate that the proposed DVPC outperforms the state-of-the-art methods in terms of reconstruction quality.

High-resolution ultrasound of spigelian and groin hernias: a closer look at fascial architecture and aponeurotic passageways.

From the clinical point of view, a proper diagnosis of spigelian, inguinal and femoral hernias may be relevant for orienting the patient’s management, as these conditions carry a different risk of complications and require specific approaches and treatments. Imaging may play a significant role in the diagnostic work-up of patients with suspected abdominal hernias, as the identification and categorization of these conditions is often unfeasible on clinical ground. Ultrasound imaging is particularly suited for this purpose, owing to its dynamic capabilities, high accuracy, low cost and wide availability. The main limitation of this technique consists of its intrinsic operator dependency, which tends to be higher in difficult-to-scan areas such as the groin because of its intrinsic anatomic complexity. An in-depth knowledge of the anatomy of the lower abdominal wall is, therefore, an essential prerequisite to perform a targeted ultrasound examination and discriminate among different types of regional hernias. The aim of this review is to provide a detailed analysis of the fascial architecture and aponeurotic passageways of the abdominal wall through which spigelian, inguinal and femoral hernias extrude, by means of schematic drawings, ultrasound images and video clips. A reasoned landmark-based ultrasound scanning technique is described to allow a prompt and reliable identification of these pathologic conditions.

Strain-Mediated High Conductivity in Ultrathin Antiferromagnetic Metallic Nitrides.

Strain engineering provides the ability to control the ground states and associated phase transition in epitaxial films. However, the systematic study of the intrinsic character and strain dependency in transition-metal nitrides remains challenging due to the difficulty in fabricating stoichiometric and high-quality films. Here the observation of an electronic state transition in highly crystalline antiferromagnetic CrN films with strain and reduced dimensionality is reported. By shrinking the film thickness to a critical value of ≈30 unit cells, a profound conductivity reduction accompanied by unexpected volume expansion is observed in CrN films. The electrical conductivity is observed surprisingly when the CrN layer is as thin as a single unit cell thick, which is far below the critical thickness of most metallic films. It is found that the metallicity of an ultrathin CrN film recovers from insulating behavior upon the removal of the as-grown strain by the fabrication of freestanding nitride films. Both first-principles calculations and linear dichroism measurements reveal that the strain-mediated orbital splitting effectively customizes the relatively small bandgap at the Fermi level, leading to an exotic phase transition in CrN. The ability to achieve highly conductive nitride ultrathin films by harnessing strain-control over competing phases can be used for utilizing their exceptional characteristics.

High-dimensional dynamic systems identification with additional constraints

This note presents a unified analysis of the identification of dynamical systems with low-rank constraints under high-dimensional scaling. This identification problem for dynamic systems is challenging due to the intrinsic dependency of the data. To alleviate this problem, we first formulate this identification problem into a multivariate linear regression problem with a row-sub-Gaussian measurement matrix using the more general input designs and the independently repeated sampling schemes. We then propose a nuclear norm heuristic method that estimates the parameter matrix of a dynamic system from a few input-state data samples. Based on this, we can extend the existing results. In this article, we consider two scenarios. (i) In the noiseless scenario, nuclear-norm minimization is introduced for promoting low-rank. We define the notion of weak restricted isometry property, which is weaker than the ordinary restricted isometry property, and show it holds with high probability for the row-sub-Gaussian measurement matrix. Thereby, the rank-minimization matrix can be exactly recovered from a finite number of data samples. (ii) In the noisy scenario, a regularized framework involving the nuclear norm penalty is established. We give the notion of operator norm curvature condition for the loss function and show it holds for the row-sub-Gaussian measurement matrix with high probability. Consequently, when specifying the suitable choice of the regularization parameter, the operator norm error of the optimal solution of this program has a sharp bound given a finite amount of data samples. This operator norm error bound is always smaller than the ordinary Frobenius norm error bound obtained in the existing work.

Dual-Band Polarization-Independent Subwavelength Grating Coupler for Wavelength Demultiplexing

Surface grating couplers are diffractive periodic structures that enable efficient coupling of light between optical fibers and planar waveguides. Conventional grating couplers have polarization specific and limited wavelength operation, because of the intrinsic radiation angle dependency on both wavelength and polarization. In this Letter, we propose, to the best of our knowledge, the first polarization-independent surface fiber-chip grating coupler, behaving as a wavelength splitter for the O and C communication bands. Polarization insensitivity is achieved by subwavelength segmentation of the silicon gratings, together with a novel design to allow the dual wavelengths to propagate along two opposite directions in the chip. For the TE and TM polarizations, coupling efficiencies around −4.5 dB are achieved at both 1310 nm and 1550 nm, with an average 1-dB bandwidth of ~45 nm and ~60 nm, respectively. This grating coupler concept can be used as a part of transceivers to increase the data rate of wavelength-division-multiplexing (WDM) systems for fiber-to-the-home (FTTH) network services.

Demystifying Deep Learning in Predictive Spatiotemporal Analytics: An Information-Theoretic Framework.

Deep learning has achieved incredible success over the past years, especially in various challenging predictive spatiotemporal analytics (PSTA) tasks, such as disease prediction, climate forecast, and traffic prediction, where intrinsic dependence relationships among data exist and generally manifest at multiple spatiotemporal scales. However, given a specific PSTA task and the corresponding data set, how to appropriately determine the desired configuration of a deep learning model, theoretically analyze the model's learning behavior, and quantitatively characterize the model's learning capacity remains a mystery. In order to demystify the power of deep learning for PSTA in a theoretically sound and explainable way, in this article, we provide a comprehensive framework for deep learning model design and information-theoretic analysis. First, we develop and demonstrate a novel interactively and integratively connected deep recurrent neural network (I2DRNN) model. I2DRNN consists of three modules: an input module that integrates data from heterogeneous sources; a hidden module that captures the information at different scales while allowing the information to flow interactively between layers; and an output module that models the integrative effects of information from various hidden layers to generate the output predictions. Second, to theoretically prove that our designed model can learn multiscale spatiotemporal dependence in PSTA tasks, we provide an information-theoretic analysis to examine the information-based learning capacity (i-CAP) of the proposed model. In so doing, we can tackle an important open question in deep learning, that is, how to determine the necessary and sufficient configurations of a designed deep learning model with respect to the given learning data sets. Third, to validate the I2DRNN model and confirm its i-CAP, we systematically conduct a series of experiments involving both synthetic data sets and real-world PSTA tasks. The experimental results show that the I2DRNN model outperforms both classical and state-of-the-art models on all data sets and PSTA tasks. More importantly, as readily validated, the proposed model captures the multiscale spatiotemporal dependence, which is meaningful in the real-world context. Furthermore, the model configuration that corresponds to the best performance on a given data set always falls into the range between the necessary and sufficient configurations, as derived from the information-theoretic analysis.

Undermining populism through Gandhi’s intercultural democratic discourse

ABSTRACT Populism is receiving increasing attention and the intrinsic dependency of populism from liberal democracy is being scrutinised to find forms able to contain the populist rise, so far with limited success. Little consideration has been given to the implications that populism is a modern political category that could not emerge without liberal democratic regimes. This article analyses the entanglement between postcolonialism, democratic theory and populism in order to fill this research gap. It focuses on the notion of demagogic populism as a threat to democratic regimes that liberal theories are incapable of contrasting. It analyses in some detail the intercultural democratic discourse (IDD) of M. K. Gandhi, in order to understand to what extent it undermines the populist upsurge and contributes to structuring a substantive and intercultural alternative democratisation theory, with respect to liberal democracy. To cope with the extent of Gandhi’s thought, after addressing some limits of liberal multiculturalism, the article focuses on four Gandhian categories: ‘civilisation’, ‘duty’, ‘non-possession individualism’ and ‘spirituality’. As Gandhi engaged in the anti-colonial struggle and critical strategic dialogues in intra-civilisational contexts (tradition, religion, caste, modes of production, etc.), he advances a solid IDD, elaborating a groundbreaking vocabulary that includes ashram, satyagraha, swaraj and swadeshi.

Restoring order to the ordo salutis: Conversion normativity in light of recent critique

Given the disparate details of Luke’s report on early conversion practices, some scholars have sought to deliver their readers from the difficulty of determining normativity. However, the complication which excites such re-exegesis, at least in terms of the ordo salutis, is often overstated. Indeed, once the intrinsic dependency of conversion-initiation’s constituent elements is considered, a general (and essential) order emerges. Even the seemingly mutable moment of regeneration is fixed given a concrete set of conditions. Thus, the only controversy remaining concerns the particulars, specifically which conditions initiate regeneration.

Uncertain Structural Free Vibration Analysis With Non-Probabilistic Spatially Varying Parameters

The uncertain free vibration analysis of engineering structures with the consideration of nonstochastic spatially dependent uncertain parameters is investigated. A recently proposed concept of interval field is implemented to model the intrinsic spatial dependency of the uncertain-but-bounded system parameters. By employing the appropriate discretization scheme, evaluations of natural frequencies for engineering structures involving interval fields can be executed within the framework of the finite element method. Furthermore, a robust, yet efficient, computational strategy is proposed such that the extreme bounds of natural frequencies of the structure involving interval fields can be rigorously captured by performing two independent eigen-analyses. Within the proposed computational analysis framework, the traditional interval arithmetic is not employed so that the undesirable effect of the interval overestimation can be completely eliminated. Consequently, both sharpness and physical feasibility of the results can be guaranteed to a certain extent for any discretized interval field. The plausibility of the adopted interval field model, as well as the feasibility of the proposed computational scheme, is clearly demonstrated by investigating both academic-sized and practically motivated engineering structures.

A New Cellular Automaton Method Coupled with a Rate-dependent (CARD) Model for Predicting Dynamic Recrystallization Behavior

A comprehensive cellular automaton (CA) model should be coupled with a rate-dependent (RD) model for analyzing the RD deformation of alloys at high temperatures. In the present study, a new CA technique coupled with an RD model—namely, CARD—was developed. The proposed CARD model was used to simulate the dynamic recrystallization phenomenon during the hot deformation of the Inconel 718 superalloy. This model is capable of calculating the mean grain size and volume fraction of dynamic recrystallized grains, and estimating the phenomenological flow behavior of the material. In the presented model, an actual orientation definition comprising three Euler angles was used by implementing the electron backscatter diffraction data. For calculating the lattice rotation of grains, it was assumed that all slip systems of grains are active during the high-temperature deformation because of the intrinsic rate dependency of the procedure. Moreover, the morphological changes in grains were obtained using a topological module.