Transformation Function Research Articles

Converting Entity-Attribute-Value Data Sources to OMOP's CDM: Lessons Learned.

Clinical data repositories often use entity-attribute-value (EAV) data models. To be valuable for secondary use, these health data can be transformed to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The present paper describes the lessons learned from such an endeavour based on the concept of registering transformation functions on source data elements. We further provide future work directions for follow-up projects.

What are the impacts of Cohesion Policy on the trade-off between employment and productivity?

ABSTRACT This paper examines the heterogeneous effectiveness of European Union (EU) Cohesion Policy in fostering regional growth, considering the potential trade-off between productivity improvement and job creation. By analysing the impact of European Structural and Investment Funds on regional development at the NUTS-2 level, the study contributes to the literature on productivity and employment trade-offs at the regional level. A novel empirical approach, based on a multi-input multi-output transformation function, is employed to explore the differing impacts of EU Cohesion Policy on gross domestic product (GDP) per employee and employment rate. The findings suggest that there exists heterogeneity (across different funds and regions) in the effects of Cohesion Policy on the trade-off between employment and productivity. Striking a balance between productivity-boosting investments and job creation is crucial in ensuring sustainable economic development and social inclusion and in reducing regional disparities.

‘You never get a livelihood out of going to school’: the crisis of urban modernity and the education-employment nexus in Harare, Zimbabwe

The economic crisis and informalisation in Zimbabwe from the late 1990s had many consequences, including the unattainability of the early postcolonial modernist lifestyle and the collapse of the robust education-employment nexus and steady transition pathway from school to work for graduates in urban areas. Education in Zimbabwe played social mobility and social transformation and reproduction functions from colonial times and continued to have an inherent value to Zimbabweans in the postcolonial period. However, with the collapse of the education-employment nexus and structural changes in the opposition movement which offered a transition path from university into party politics until the late 2000s, many school and university graduates did not get to experience social mobility. Accompanied by the wider frustrating conditions, such as bad governance, corruption and economic decline, this resulted in young people reconsidering the social mobility function of education and challenging modernist premises about education. These premises had been reproduced at different levels from family to wider society, and young people started rethinking and challenging them through their narratives, considering curriculum changes to include entrepreneurship education, participation in the informal economy and protest movements, all of which challenge the postcolonial modernity framework.

DNIM: Deep-sea netting intelligent enhancement and exposure monitoring using bio-vision

Intelligent monitoring of deep-sea nets is affected by light attenuation, light scattering, and limited dynamic range factors of the camera, which can cause color shift, low visibility, and over/underexposure in monitoring, resulting in reduced farming efficiency, and misdetection of biological behavior. Therefore, we propose a method for underwater net tank scene enhancement and exposure using multicolor space and bio-vision, called DNIM. Specifically, we first propose an intelligent enhancement and exposure monitoring method for deep-sea nets using bio-vision, which eliminates the effect of exposure on underwater imaging by introducing a bio-vision system that uses a brightness transformation function to generate a series of exposure images. Inspired by the image fusion method, we designed a color correction and visibility recovery method using bio-vision. In the Lab color model, color shifts in bio-vision are corrected by guiding feature transformations with contrast-constrained adaptive histogram equalization; in the RGB color model, depth information is recovered by creating a linear model for the depth of field of underwater blurred images based on the characteristics of the bio-vision system. To obtain more qualified results, we also propose an efficient perceptual fusion module to mix the output of visibility recovery and color correction. Finally, we propose two deep-sea net tank culture datasets: salmon bait-eating behavior dataset (SBBD) and salmon non-bait-eating behavior dataset (SNBD). By comparing the experimental analysis in four real scenario datasets, our DNIM achieved superior results compared to 16 advanced underwater enhancement methods. The code is publicly available at: https://github.com/An-Shunmin/DNIM.

Modelling the VaR Using Integral Probability and Tail Dependence

In this paper, a method is proposed to estimate value at risk (VaR) in a multivariate context by employing the probability integral transformation (PIT) and tail dependence function to model extreme dependence structure. The proposed method involves two vectorial measures of VaR, which use either the distribution function or the survival function. Moreover, properties of these risk measures are analysed, and a connection between them and tail dependence functions is established.

Dynamic event-driven optimal consensus control for state-constrained multiagent zero-sum differential graphical games

In this paper, a dynamic event-driven optimal control scheme is proposed for the zero-sum differential graphical games in nonlinear multiagent systems with full-state constraints. Initially, to address the dual demands of optimality and state constraints, a set of system transformation functions are introduced to satisfy the state constraints of the agents. Then, by applying the principle of differential game theory, the distributed optimal control problem affected by external disturbances is formulated as a zero-sum differential graphical game, and the performance index function related to neighbor informations and disturbances is designed for each follower. Afterwards, to enhance the utilization of communication resource, a novel dynamic event-triggered mechanism characterized by a dynamic threshold parameter and an auxiliary dynamic variable is developed, which not only exhibits greater flexibility but also diminishes the frequency of triggers. Furthermore, the approximate optimal control strategies are obtained by employing an event-driven adaptive dynamic programming algorithm. Ultimately, a simulation example is presented to verify the applicability of the proposed control approach.

Predicting the entire pyrolysis process of representative charring material infiltrated with kerosene using an improved artificial neural network

It is crucial to predict the entire pyrolysis process of charring materials infiltrated with flammable liquids to provide guidance for reducing the fire hazards and implementing numerical simulations of fire accidents caused by the leakage of flammable liquids. In the present study, pyrolysis data obtained from the thermogravimetric experiments were preprocessed using linear interpolation and data normalization methods. The processed data set was then used to train the artificial neural network (ANN) framework to predict the pyrolysis behaviors of typical charring material (Mongolian Scots pine) infiltrated with typical flammable liquid (kerosene) under three different conditions: (1) pyrolysis at a constant mass fraction of kerosene with multiple heating rates; (2) pyrolysis at multiple mass fractions of kerosene with a constant heating rate; and (3) pyrolysis at multiple mass fractions of kerosene and multiple heating rates. The ANN, with the double hidden layer structure (6−3), was capable to well predict the entire pyrolysis behaviors of Mongolian Scots pine under all the three scenarios. Besides, five data transformation function (multiplication, exponential function with base e, logarithmic function with base e, square root, and exponentiation function) were applied to the present three input variables (temperature, mass fraction of kerosene, and heating rate) to generate new input variables in order to extract more characteristics among the pyrolysis data. Therein, the ANN utilizing the data transformation functions of multiplication and exponential function with base e exhibited the best predictive ability. Among the three input variables in the ANN (temperature, mass fraction of kerosene, and heating rate), temperature was identified as the most sensitive one to the prediction performance, followed by mass fraction of kerosene, with heating rate being the least sensitive.

Composite learning prescribed performance control of strict-feedback nonlinear systems with mismatched parametric uncertainties

A composite learning prescribed performance control (CLPPC) approach is presented for strict-feedback nonlinear systems with mismatched parametric uncertainties. A finite-time performance function based on polynomial is introduced to predefined a restriction region with respect to the tracking error. By introducing an error transformation function, the tracking error restriction problem of the original system is transformed into the stability problem of an equivalent transformation system. To guarantee the convergence of unknown parameters, online recording data and instantaneous are used to generate a prediction error, which is used together with filtered tracking errors to update parameter estimations under an interval excitation condition. All signals are proven to be ultimately uniformly stable under the proposed CLPPC strategy. Furthermore, the tracking error is limited within the predefined region after the predefined time. Simulation results verify the effectiveness of the proposed approach.

Three-dimensional adaptive dynamic surface guidance law for missile with terminal angle and field-of-view constraints

In this paper, an adaptive dynamic surface (DSC) guidance law for missile is designed to intercept the maneuvering target with field-of-view (FOV) and terminal angle constraints in three-dimensional(3D) space, and the missile autopilot dynamics is considered. Firstly, the time-varying transformation function related to line of sight (LOS) is used to replace the FOV constraints, transforming the process-constrained control problem into the output-constrained control problem. Meanwhile, the 3D coupled relative kinematics model considering missile autopilot dynamics and maneuvering target acceleration is established. Secondly, a novel time-varying asymmetric barrier Lyapunov function (TABLF) with dead-zone characteristics is introduced to the adaptive dynamic surface guidance law design process to improve the robustness of parameter debugging. Thirdly, with the help of a nonlinear adaptive filter, the ‘explosion of complexity’ problem can be avoided effectively, which is caused by analytic computation of virtual signal derivatives. Furthermore, aiming at the problem of autopilot dynamic errors, target acceleration disturbances, and unmeasurable parameters in the model, a novel adaptive law is used to evaluate online. Then, the stability of the closed-loop system is rigorously proven using Lyapunov criteria. Ultimately, Numerical simulations with various constraints and comparison studies have been considered to show the feasibility and effectiveness of the proposed missile guidance law.

Abstract Tu096: The Aryl Hydrocarbon Receptor Agonist, L-Kynurenine, Modulates Cardiac Fibroblast Activation and Antigen Presentation

Background: The Aryl Hydrocarbon Receptor (AHR) is a ubiquitously expressed transcription factor that regulates the cellular response to external stimuli through the binding of ligands including tryptophan metabolites such as L-Kynurenine (L-Kyn). Alterations in tryptophan metabolic pathways and downregulation of myocardial AHR are associated with cardiac inflammation and fibrosis in experimental heart failure (HF) induced by transverse aortic constriction (TAC). We hypothesized cardiac fibroblast (CFB) AHR and its agonism by L-Kyn prevent the pro-fibrotic and pro-inflammatory activity of CFBs in experimental HF. Methods and Results: We performed TAC or sham surgery on Ahr -/- mice and WT littermates for 4 weeks and found that Ahr-/- mice showed significantly decreased contractile function measured by echocardiography, and increased collagen deposition in left ventricular histological sections after TAC compared to WT mice. We performed bulk RNA sequencing in sorted CFB (CD31-CD45-MEFSK4+) from Ahr -/- and WT mice. Gene set enrichment analysis showed Ahr -/- CFB had increased expression in pathways for fibroblast activation, chemokine production, and immune cell recruitment. To investigate the role of L-Kyn agonism of CFB AHR in transformation and contractile function, murine CFB were treated with Transforming Growth Factor β (TGFβ) (100ng/ml) or seeded in collagen discs, in the presence or absence of L-Kyn(100ug/ml). L-Kyn attenuated TGFβ mediated induction of αSMA and collagen 1 protein expression by immunofluorescence and western blot, as well as decreased disc contraction 72h after mechanical activation and release. The role of AHR agonism by L-Kyn on the pro-inflammatory function of CFB was determined in WT CFB treated with IFNγ (100U/ml) in the presence or absence of L-Kyn. IFNγ induction of chemokines Cxcl9 and Cxcl10, as well as MHC-II surface expression were attenuated by L-Kyn, so was T cell adhesion to CFB in co-culture assays. In contrast, L-Kyn had no effect on the ability of CFB to take up and process antigens, as determined by DQ-Ovalbumin internalization quantified by flow cytometry. Conclusions: Our results demonstrate that AHR regulates activation and pro-inflammatory signatures in CFB at baseline, and that stimulation of CFB transformation and pro-inflammatory activity is dampened by agonism with L-Kyn. Current studies are aimed at investigating AHR agonism in the context of pressure overload induced HF in CFB reporter mice.

Fast, Zero-Reference Low-Light Image Enhancement with Camera Response Model.

Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method's advantages, both subjectively and objectively.

An energy consumption field model considering motion position errors for energy efficient machining on CNC machines:CNC programming perspective

Motion position errors, which are caused by factors such as machine tool assembly accuracy and thermal effects, often result in deviations in both machining energy consumption and precision, compared to what is expected. Furthermore, the degree of deviations dynamically varies with the machining schemes. These issues hinder the implementation of high-quality energy-saving machining. To address this challenge, an energy consumption field model is proposed, reflecting the impact of motion position errors on energy consumption and precision variations in different machining scenarios. Firstly, by analysing the conversion process from process plans to programming instructions, a dynamic cascading mechanism is revealed, driven by motion position errors that impact tool cutting trajectory offset, machining energy consumption and precision deviation. Subsequently, a dynamic quantification model for the cutting trajectory offset at the programming origin is established by correlating static geometric errors and dynamic spindle rotation errors with the programming origin coordinates. Finally, a transformation function is established by using the cutting trajectory offset as an intermediate variable to correlate motion position error, machining energy consumption and precision deviation. A spatial energy consumption field model is developed. Experimental results indicate that, while meeting precision constraints in the XYZ directions, a 10.96% reduction in energy consumption is achieved through the correction of programming origin coordinates.

Correlated Anion Disorder in Heteroanionic Cubic TiOF2.

Resolving anion configurations in heteroanionic materials is crucial for understanding and controlling their properties. For anion-disordered oxyfluorides, conventional Bragg diffraction cannot fully resolve the anionic structure, necessitating alternative structure determination methods. We have investigated the anionic structure of anion-disordered cubic (ReO3-type) TiOF2 using X-ray pair distribution function (PDF), 19F MAS NMR analysis, density functional theory (DFT), cluster expansion modeling, and genetic-algorithm structure prediction. Our computational data predict short-range anion ordering in TiOF2, characterized by predominant cis-[O2F4] titanium coordination, resulting in correlated anion disorder at longer ranges. To validate our predictions, we generated partially disordered supercells using genetic-algorithm structure prediction and computed simulated X-ray PDF data and 19F MAS NMR spectra, which we compared directly to experimental data. To construct our simulated 19F NMR spectra, we derived new transformation functions for mapping calculated magnetic shieldings to predicted magnetic chemical shifts in titanium (oxy)fluorides, obtained by fitting DFT-calculated magnetic shieldings to previously published experimental chemical shift data for TiF4. We find good agreement between our simulated and experimental data, which supports our computationally predicted structural model and demonstrates the effectiveness of complementary experimental and computational techniques in resolving anionic structure in anion-disordered oxyfluorides. From additional DFT calculations, we predict that increasing anion disorder makes lithium intercalation more favorable by, on average, up to 2 eV, highlighting the significant effect of variations in short-range order on the intercalation properties of anion-disordered materials.

Adaptive event-triggered finite-time prescribed performance control of PMSM stochastic system considering time-varying delays

This paper investigates the finite-time prescribed performance tracking control problem of permanent magnet synchronous motor (PMSM) considering stochastic disturbances and time-varying delays under event-triggered mechanism. A quintuple polynomial finite-time prescribed performance function (FPPF) is introduced to ensure the transient and steady-state performance of the system output, and a nonlinear transformation function is employed to convert the constrained error into an unconstrained one. The Lyapunov-Krasovskii function is constructed to address time delays. And the system uncertainties are approximated by the radial basis function neural networks (RBFNN). For the “explosion of complexity” caused by backstepping method, a tracking differentiator (TD) is employed. By combining finite time control, command filtering backstepping control, and event-triggered mechanism, the effect of the filter errors is decreased, and the update frequency of the control signals are reduced. It is shown that the proposed controller can guarantee finite time convergence bounded of all signals in the closed-loop system, and the tracking error can converge in finite time. Finally, simulation results are presented to illustrate the effectiveness of the proposed controller.

Two-stage transfer learning-based nonparametric system identification with Gaussian process regression

Most system identification methods ignore correlations between different identification tasks and do not make full use of historical models when identifying a new process. In this paper, a two-stage transfer learning-based nonparametric system identification method is proposed to improve model accuracy and to reduce identification costs. First, to overcome the transfer capability limitations of the discrete-time model, the target process is modeled as a continuous-time impulse response (IR) function, which consists of a transfer model part and a residual model part, which is regarded as a zero-mean Gaussian process (GP). Then, by utilizing the process geometrical characteristics, the IR functions are classified into three IR domains, and transformation functions within domain or between domains are designed to learn the relationship between the source process model and the target process model. Finally, a two-stage nonparametric identification algorithm based on GP regression is developed: The first stage is performed to select the appropriate type of transformation function through the weighted-derivative dynamical time warping technique, and the second stage is conducted to estimate the transfer model and residual model by using the empirical Bayes approach. Three case studies are conducted to validate the superiority of the proposed identification method.

Stabilized GAN models training with kernel-histogram transformation and probability mass function distance

Image generation using generative adversarial networks (GANs) has been extensively researched in recent years. Despite active developments, the chronic issue of training instability in GANs remains unresolved. To alleviate this problem, this study proposes a model named probability mass function GANs (PMF-GAN), which handles the inherent limitation of GANs. The PMF-GAN framework employs kernels, histogram transformation, and probability mass function (PMF) distance for distribution learning. The configuration of PMF-GAN kernel and PMF distance offers flexibility, allowing for optimal settings tailored to datasets and experimental environments. In this study, experiments were conducted using the gaussian kernel across five different distances. The experiments demonstrated that PMF-GAN outperforms the baselines in terms of visual quality and evaluation metrics, such as Inception score and Frechet Inception distance (FID). For example, in the CIFAR-10 dataset, Euclidean-based PMF-GAN applying with 3 bins showed a 21.5 % and 32.8 % improvement in Inception score and FID, respectively, compared to conventional WGAN-GP. Similarly, in the AFHQ dataset with the same settings, the improvements were 56.9 % and 61.5 %. As a result, this study presents the potential to achieve stable training processes in GAN models with modified loss function structures. The flexibility of the proposed model allows for simultaneous application to various models, contributing to the overall improvement of generative model training processes in the future.

Diffusion tensor transformation for personalizing target volumes in radiation therapy

Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy. The ICT model allows the mapping of tumor cell densities and tumor fronts (as iso-levels of tumor cell density) from the template image to the patient image for inclusion in radiotherapy treatment planning. The proposed approach transforms the diffusion tensors to simulate tumor growth in locally deformed anatomy and outputs the tumor cell density distribution over time. The ICT model is validated in a cohort of ten brain tumor patients. Comparative analysis with the tumor cell density in the original template image shows that the ICT model accurately simulates tumor cell densities in the deformed image space. By creating radiotherapy target volumes as tumor fronts, this study provides a framework for more personalized radiotherapy treatment planning, without the use of additional imaging.

Dynamical analysis and event-triggered adaptive finite-time prescribed performance control of the FO coupled MEMS resonators

This paper explores the dynamic behaviors and control method of weakly coupled micro-electro-mechanical system (MEMS) resonators with fractional-order (FO) dynamics. In the dynamics analysis session, Lyapunov exponents, bifurcation theory, and phase diagrams are used to analyze the effects of system parameters, coupling stiffness, and FO on the complex dynamical behaviors such as periodicity, pseudo-periodicity, and chaos oscillations. To suppress chaotic oscillations, a dynamic event-triggered finite-time prescribed performance controller is proposed. The unknown nonlinear functions are approximated through the interval type-3 fuzzy system (IT3FS) with an adaptive law. A novel finite-time prescribed performance function (finite-time PPF) is constructed to establish constrained boundaries for tracking errors. Compared with the existing PPFs, the proposed approach allows for more flexible setting of the convergence boundary before reaching steady-state. Subsequently, the constrained tracking errors are mapped to an unconstrained form using a nonlinear transformation function, whether the error constraint is symmetric or asymmetric. To circumvent the “explosion of complexity” arising from backstepping design process, a tracking differentiator (TD) is utilized. Additionally, to alleviate strain on communication resources, an event-triggered mechanism is devised to update control signals. The trigger threshold of the control signals can be adaptively adjusted based on the values of the Lyapunov function and the dynamic auxiliary variables. This control method guarantees finite-time convergence for all signals of the system. Finally, extensive simulations are performed to verify the effectiveness of the proposed algorithm.

Improving PRISMA hyperspectral spatial resolution and geolocation by using Sentinel-2: development and test of an operational procedure in urban and rural areas

Hyperspectral (HS) satellites like PRISMA (PRecursore IperSpettrale della Missione Applicativa) offer remarkable capabilities, yet they are constrained by a relatively coarse spatial resolution, curbing their efficacy in those applications that require pinpoint accuracy. Here we propose a fusion process, aimed at the enhancement of PRISMA HS spatial resolution by using the spatial and spectral information of Sentinel-2 multispectral (MS) data (HS-MS fusion process), validated against four airborne HS flights simultaneous to satellite overpasses on different land use distributions. Adopting the PRISMA panchromatic (PAN) image, the proposed solution was also compared with the results of a HS-PAN pansharpening process. A two-steps operational workflow is proposed, based on two state-of-the-art and open-source algorithms. The first step consisted of the geocoding of PRISMA L2 products using Senintel-2 as reference and was accomplished with the phase-based algorithm implemented in AROSICS (Automated and Robust Open-Source Image Co-registration Software). The geometric displacement in L2 data was found to be between 80 m and 250 m, irregularly spatially distributed throughout the same scene and among scenes, and it was corrected by means of thousands of regularly spatially distributed tie points. A second-order polynomial transformation function was integrated in the algorithm. The second step consisted of employing the HySure (HS Super resolution) fusion algorithm to perform both the HS-MS fusion and the HS-PAN pansharpening, returning a PRISMA HS improved dataset with a spatial resolution of 10 m and 5 m, respectively. Four different per-band accuracy metrics were used to evaluate the accuracy of both products against airborne data. Overall, HS-MS data achieved increased accuracy in all validation metrics, i.e. + 28 % (root mean square error, RMSE), +23 % (spectral angle mapper, SAM), +7% (peak signal-to-noise ratio, PSNR) and + 11 % (universal image quality index, UIQI), with respect of HS-PAN data. These outcomes showed that using the spectral information of Sentinel-2 both spectral and spatial patterns were reconstructed more consistently in three different urban and rural scenarios, avoiding the presence of blur and at-edge artefacts as opposed to HS-PAN pansharpening, therefore suggesting an optimal strategy for satellite HS data resolution enhancement.

Fluoride induces immunotoxicity by regulating riboflavin transport and metabolism partly through IL-17A in the spleen

The impairment of the immune system by fluoride is a public health concern worldwide, yet the underlying mechanism is unclear. Both riboflavin and IL-17A are closely related to immune function and regulate the testicular toxicity of fluoride. However, whether riboflavin or IL-17A is involved in fluoride-induced immunotoxicity is unknown. Here, we first established a male ICR mouse model by treating mice with sodium fluoride (NaF) (100 mg/L) via the drinking water for 91 days. The results showed that fluoride increased the expression of the proinflammatory factors IL-1β and IL-17A, which led to splenic inflammation and morphological injury. Moreover, the expression levels of the riboflavin transporters SLC52A2 and SLC52A3; the transformation-related enzymes RFK and FLAD1; and the key mitochondrial functional determinants SDH, COX, and ATP in the spleen were measured via real-time PCR, Western blotting, and ELISA. The results revealed that fluoride disrupted riboflavin transport, transformation, metabolism, and mitochondrial function. Furthermore, wild-type (WT) and IL-17A knockout (IL-17A-/-) C57BL/6 J male mice of the same age were treated with NaF (24 mg/kg·bw, equivalent to 100 mg/L) and/or riboflavin sodium phosphate (5 mg/kg·bw) via gavage for 91 days. Similar parameters were evaluated as above. The results confirmed that fluoride increased riboflavin metabolism through RFK but not through FLAD1. Fluoride also affected mitochondrial function and activated neutrophils (marked with Ly6g) and macrophages (marked with CD68) in the spleen. Interestingly, IL-17A partly mediated fluoride-induced riboflavin metabolism disorder and immunotoxicity in the spleen. This work not only reveals a novel toxic mechanism for fluoride but also provides new clues for exploring the physiological function of riboflavin and for diagnosing and treating the toxic effects of fluoride in the environment.