Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Neural Correlates in the Prefrontal Cortex during a Delay Discounting Task in Healthy Adults: An fNIRS Study

The phenomenon of future rewards being devalued as a function of delay is referred to as delay discounting (DD). It is considered a measure of impulsivity, and steep DD characterizes psychiatric problems such as addictive disorders and attention deficit/hyperactivity disorder. This preliminarily study examined prefrontal hemodynamic activity using functional near-infrared spectroscopy (fNIRS) in healthy young adults performing a DD task. Prefrontal activity during a DD task with hypothetical monetary rewards was measured in 20 participants. A discounting rate (k-value) in the DD task was determined on the basis of a hyperbolic function. To validate the k-value, a DD questionnaire and the Barratt impulsiveness scale (BIS) were administered after fNIRS. The DD task induced a significant increase in oxygenated hemoglobin (oxy-Hb) concentration bilaterally in the frontal pole and dorsolateral prefrontal cortex (PFC) compared with a control task. Significant positive correlations were detected between left PFC activity and discounting parameters. Right frontal pole activity, however, showed significantly negative correlation with motor impulsivity as a BIS subscore. These results suggest that left and right PFCs have differential contributions when performing the DD task. The present findings suggest the idea that fNIRS measurement of prefrontal hemodynamic activity can be useful for understanding the neural mechanisms underlying DD and is applicable for assessing PFC function among psychiatric patients with impulsivity-related problems.
Relations on Sets

This paper is dedicated to the memory of George Boole FRS (1815-1864), first Professor of Mathematics at University College Cork. A relation on a non-empty set K is any subset W of the cartesian product K × K. If (x, x) ∈ W for every x ∈ K, W is said to be reflexive on K. If for x, y, z ∈ K, (x, y) ∈ W implies that (y, x) ∈ W, W is said to be symmetric on K. If for x, y, z ∈ K (x, y) ∈ W and (y, z) ∈ W together imply that (x, z) ∈ W, W is said to be transitive on K. Finally, if the relation W is reflexive, symmetric and transitive on K, W is said to be an equivalence relation on K. We contend that equivalence relations are of central importance in many areas of mathematics. Many texts on algebra (for example Topics in Algebra [1]) contain as an exercise the following fallacious argument which the reader is challenged to refute.
Detection of low-frequency oscillations in neonatal piglets with speckle contrast diffuse correlation tomography

Abstract. Significance Low-frequency oscillations (LFOs) (<0.1 Hz) with respect to cerebral blood flow (CBF) have shown promise as an indicator of altered neurologic activity in the abnormal brain. Portable optical instruments have evolved to offer a noninvasive alternative for continuous CBF monitoring at the bedside compared with many large neuroimaging modalities. However, their utilization for acquiring LFOs of CBF has only been studied to a limited extent. Aim We aim to optimize an innovative speckle contrast diffuse correlation tomography (scDCT) system for the detection of LFOs within CBF variations. Approach The scDCT was optimized to achieve a higher sampling rate and a faster image reconstruction using a moving window 3D reconstruction algorithm with parallel computation. Power spectral density (PSD) analysis was performed to investigate altered LFOs during transient global cerebral ischemia in neonatal piglets. Results Transient global cerebral ischemia resulted in reductions in both CBF and PSD compared with their baseline values. Conclusions Spontaneous LFOs, combined with CBF, provide a more comprehensive assay with the potential to clarify pathological mechanisms involved in brain injury. These results support scDCT’s inclusion and application in the growing area of LFO analysis and demonstrate its inherent advantage for neurological studies in preclinical and clinical settings, such as neonatal intensive care units.
Trait Mindfulness and Counterproductive Work Behavior: The Role of Stop and Start Control

This study aimed to examine the antecedents of Counterproductive Work Behavior (CWB) by investigating two dimensions of self-control, namely stop and start control, as mediators between trait mindfulness and CWB. A correlational research design was employed, and the participants were full-time workers aged between 20 and 39 years. The instruments consisted of the Stop Control and Start Control Scale, Mindfulness Attention Awareness Scale (MAAS), and Counterproductive Work Behavior Checklist (CWB-C). The results of the mediation analysis showed that the trait mindfulness had an indirect effect on CWB through stop control (ab = -.11, p < .01) and start control (ab = -.04, p < .01), as well as a direct effect on CWB (c’ = -.07, p < .01; c’ = -.24, p < .01). These findings suggested that stop and start control partially mediated the relationship between trait mindfulness and CWB.
Staying in Touch with Affect: Maintaining Vital Access to the Body while Working Online

Presented here is a verbatim session conducted online. The session integrates the ideas of Aline LaPierre’s therapeutic touch model into psychoanalytic practice. Allan Schore introduced LaPierre’s work in Psychologist/Psychoanalyst in 2003, arguing that, “it is time to reappraise the central role of the operations of the bodily self in psychopathogenisis and treatment” (p. 9). More specifically, he wrote, “Whatever the nature of the clinical issues, there is now solid evidence for the critical role of touch in human psychology and biology” (p. 9). This article presents a somatically informed, right hemispheric way of relating that enlivens and deepens the clinical process. Importantly, it presents a perspective that responds to Russell’s (2015) unease about the loss of functional equivalence as we move our face-to-face sessions online. In particular, with its focus on engaging affect at the somatic level, it addresses Russell’s concern about the loss of the fast paced, body-to body implicit processes, which she argues results in the loss of “the kind of holding environment that supports the in-dwelling of the psyche in the soma” (Bayles, 2016, p. 654).
Towards the numerical simulation of ship-generated waves using a Cartesian cut cell-based free surface solver

The Cartesian Cut Cell method has been applied to different flow configurations by researchers at the Centre of Mathematical Modelling and Flow Analysis. This method has been implementer to define flow domains around obstacles using a Godunov-type high order upwind scheme to solve Shallow Water Equations and Navier-Stokes (Euler) equations in two phase flows. A new idea to study Navier-Stoke (Euler) equations in just one phase flows where the domain is accurate described using the Cartesian Cut Cell Method around the moving free surface is presented. The solution technique involves three stages for every time step: the definition of the domain, the solution of the flow equations and the movement of the free surface. The Cartesian Cut Cell Method only requires to recompute cells affected by the movement of the free surface obtaining providing quickly the new domain. The flow equations are solved using the Artificial Compressibility Method and a Godunov-type high order upwind scheme involving the solution of Riemann problems. The Heigh Function method is applied to study the evolution on time of the free surface. This method involves the solution of the kinematic equations, where a fourth order Runge-Kutta method is employed. Boundary conditions at the free surface are discussed. The technique proposed is very quick and allows the use of big time steps. In comparison with the two phase version, the proposed techniques used one thousand times bigger time steps and require around 25 times less computational effort. On the other hand, the results shows dependency on the artificial compressibility parameter introduced as part of the solution of the flow equations. Extensions to the presented study are proposed including the use of different flow solvers. An algorithm to solve free surface flows in a single phase system is presented. The Cartesian Cut Cell Method is used to define the grid in a domain involving free surface and/or the presence of an obstacle. The algorithm approximate the solutions of the incompressible Navier-Stokes equations based on the Artificial Compressibility Method and uses the cell-centred finite volume approach. A Godunov-type high order upwind scheme is applied to compute fluxes at cell interfaces, involving polynomial reconstruction and the solution of a Riemann problem. The HLL Riemann solver and Roe’s Riemann solver are implemented as part of the Godunov-type upwind scheme. An implicit scheme is used for the time discretization in problems without free surface while an explicit forth order Runge-Kutta method is used in free surface problems. An introduction to problem where the free surface and the obstacle cut each other is presented.
Dynamic impact analysis of AISI 316L coated with Al-5Mg in air and seawater

ABSTRACT In this study, the impact wear behaviour of Al-5Mg, used as a corrosion protective coating was investigated in seawater and air environments. Tests were conducted for up to 105 cycles on the plane surface of Al-5Mg coated AISI 316L samples. Wear at the frequency of 2.5 Hz kept the coating thickness at a reliable level. However, the wear that occurred under the frequency of 5Hz reached the substrate surface. In the experiments conducted in seawater, smoother wear scars were formed compared to those in the air environment tests. The coating particles separated by the wear effect were compressed by the impactor on the wear surface with repetitive impacts, causing delamination. Friction oxidation was formed on wear scars. The hydrodynamic effect of seawater reduced the delamination by removing the separated coating particles from the surface. Thus, in seawater, 70% more wear occurred compared to in the air environment.
Regime dependence of cloud condensate variability observed at the Atmospheric Radiation Measurement Sites

Microphysical processes and cloud–radiation interaction occur on spatial scales of variability smaller than those represented explicitly in global weather forecasting and climate models. It is therefore necessary to parametrize the unresolved heterogeneity of humidity and cloud condensate in order to predict process rates accurately. Ground‐based observations from the Atmospheric Radiation Measurement sites located in various climatic regions of the world provide a source of high‐temporal‐resolution observations of cloud condensate. A number of different retrieval products for cloud condensate are assessed for the different geographical regions, years and seasons. The retrieval reliability varies with cloud type, but for cloud categories largely unaffected by precipitation a comparison across sites and longer time periods is possible. These observations confirm previously documented variability behaviour as a function of cloud fraction, but also reveal a systematic regime dependence that is not captured by existing parametrizations. Condensate variability measured as a fractional standard deviation (FSD) in warm boundary‐layer clouds is greater in the Tropics than in mid and high latitudes for scenes with comparable cloud type and fraction, with the observed FSD varying from 1.2 in the Tropics to 0.4 in the Arctic. A parametrization of the FSD of cloud liquid condensate based on the grid‐box mean total water amount and cloud fraction is formulated and shown to capture the observed range of FSD values better across different geographical sites and different seasons. The regime dependence of FSD for cirrus cloud is less pronounced than that for liquid clouds and is found largely to agree with FSD values previously derived from satellite observations.
Tunable neural networks for CT image formation

Abstract. Optimization of CT image quality typically involves balancing variance and bias. In traditional filtered back-projection, this trade-off is controlled by the filter cutoff frequency. In model-based iterative reconstruction, the regularization strength parameter often serves the same function. Deep neural networks (DNNs) typically do not provide this tunable control over output image properties. Models are often trained to minimize the expected mean squared error, which penalizes both variance and bias in image outputs but does not offer any control over the trade-off between the two. We propose a method for controlling the output image properties of neural networks with a new loss function called weighted covariance and bias (WCB). Our proposed method uses multiple noise realizations of the input images during training to allow for separate weighting matrices for the variance and bias penalty terms. Moreover, we show that tuning these weights enables targeted penalization of specific image features with spatial frequency domain penalties. To evaluate our method, we present a simulation study using digital anthropomorphic phantoms, physical simulation of CT measurements, and image formation with various algorithms. We show that the WCB loss function offers a greater degree of control over trade-offs between variance and bias, whereas mean-squared error provides only one specific image quality configuration. We also show that WCB can be used to control specific image properties including variance, bias, spatial resolution, and the noise correlation of neural network outputs. Finally, we present a method to optimize the proposed weights for a spiculated lung nodule shape discrimination task. Our results demonstrate this new image quality can control the image properties of DNN outputs and optimize image quality for task-specific performance.
Tracking Dynamic Features in Image Sequences.

This dissertation deals with detecting and tracking dynamic features in image sequences using digital image analysis algorithms. The tracking problem is complicated in oceanographic images due to the dynamic nature of the features. Specifically, the features of interest move, change size and shape. In the first part of the dissertation, the design and development of a new segmentation algorithm, Histogram-based Morphological Edge Detector (HMED), is presented. Mathematical morphology has been used in the past to develop efficient and robust edge detectors. But these morphological edge detectors do not extract weak gradient edge pixels, and they introduce spurious edge pixels. The primary reason for this is due to the fact that the morphological operations are defined in the domain of a pixel's neighborhood. HMED defines new operations, namely H-dilation and H-erosion, which are defined in the domain of the histogram of the pixel's neighborhood. The motivation for incorporating the histogram into the dilation and erosion is primarily due to the rich information content in the histogram compared to the one available in the pixel's neighborhood. As a result, HMED extracts weak gradient pixels while suppressing the spurious edge pixels. An extensive comparison of all morphological edge detectors in the context of oceanographic digital images is also presented. In the second part of the dissertation, a new augmented region and edge segmentation technique for the interpretation of oceanographic features present in the AVHRR image is presented. The augmented technique uses a topography-based method that extracts topolographical labels such as concave, convex and flat pixels from the image. In this technique, first a bicubic polynomial is fitted to a pixel and its neighborhood, and topolographical label is assigned based on the first and second directional derivatives of the polynomial surface. Second, these labeled pixels are grouped and assembled into edges and regions. The augmented technique blends the edge and region information on a proximity based criterion to detect the features. A number of experimental results are also provided to show the significant improvement in tracking the features using the augmented technique over other previously designed techniques.
Full-length transcriptome analysis of Zanthoxylum nitidum (Roxb.) DC.

Zanthoxylum nitidum (Roxb.) DC. (Z. nitidum) is a type of Chinese Dao-di herb, also called Liangmianzhen, which is widely used to treat arthralgia, rheumatic arthralgia, and stomach pain. However, genomic resources for Z. nitidum are still scarce. This study provides transcriptomic resources for Z. nitidum by applying single-molecule real-time (SMRT) sequencing technology. In total, 456,109 circular consensus sequencing (CCS) reads were generated with a mean length of 2,216 bp from Z. nitidum roots, old stems, young branches, leaves, flowers, and fruits. Of these total reads, 353,932 were full-length nonchimeric (FLNC) reads with an average length of 1,996 bp. A total of 16,163 transcripts with a mean length of 1,171 bp were acquired. Of these transcripts, 14,231 (88%) were successfully annotated using public databases. Across all the 16,163 transcripts, we identified 6,255 long non-coding RNAs (lncRNAs) and 22,780 simple sequence repeats (SSRs). Furthermore, 3,482 transcription factors were identified. Among the SSR loci, 1–3 nucleotide repeats were dominant, occupying 99.36% of the total SSR loci, with mono-, di-, and tri-nucleotide repeats accounting for 61.80%, 19.89%, and 5.02% of the total SSR loci, respectively. A total of 36 out of 100 randomly selected primer pairs were verified to be positive, 20 of which showed polymorphism. These findings enrich the genetic resources available for facilitating future studies and research on relevant topics such as population genetics in Z. nitidum.
On a Theorem of Beurling and Livingston

In their paper (1), Beurling and Livingston established a generalization of the Riesz-Fischer theorem for Fourier series in Lp using a theorem on duality mappings of a Banach space B into its conjugate space B*. It is our purpose in the present paper to give another proof of this theorem by deriving it from a more general result concerning monotone mappings related to recent results on non-linear functional equations in Banach spaces obtained by the writer (2, 3, 4, 5) and G. J. Minty (6).
Long Prediction Horizon FCS-MPC for Power Converters and Drives

Finite control set model predictive control (FCS-MPC) is a salient control method for power conversion systems that has recently enjoyed remarkable popularity. Several studies highlight the performance benefits that long prediction horizons achieve in terms of closed-loop stability, harmonic distortions, and switching losses. However, the practical implementation is not straightforward due to its inherently high computational burden. To overcome this obstacle, the control problem can be formulated as an integer least-squares optimization problem, which is equivalent to the closest point search or closest vector problem in lattices. Different techniques have been proposed in the literature to solve it, with the sphere decoding algorithm (SDA) standing out as the most popular choice to address the long prediction horizon FCS-MPC. However, the state of the art in this field offers solutions beyond the conventional SDA that will be described in this article alongside future trends and challenges in the topic.
Enhanced Solubility and Stability of Aripiprazole in Binary and Ternary Inclusion Complexes Using Hydroxy Propyl Beta Cyclodextrin (HPβCD) and L-Arginine

The low water solubility of an active pharmaceutical ingredient (aripiprazole) is one of the most critical challenges in pharmaceutical research and development. This antipsychotic drug has an inadequate therapeutic impact because of its minimal and idiosyncratic oral bioavailability to treat schizophrenia. The main objective of this study was to improve the solubility and stability of the antipsychotic drug aripiprazole (ARP) via forming binary as well as ternary inclusion complexes with hydroxypropyl-β-cyclodextrin (HPβCD) and L-Arginine (LA) as solubility enhancers. Physical mixing and lyophilization were used in different molar ratios. The developed formulations were analyzed by saturation solubility analysis, and dissolution studies were performed using the pedal method. The formulations were characterized by FTIR, XRD, DSC, SEM, and TGA. The results showcased that the addition of HPβCD and LA inclusion complexes enhanced the stability, in contrast to the binary formulations and ternary formulations prepared by physical mixing and solvent evaporation. Ternary formulation HLY47 improved dissolution rates by six times in simulated gastric fluid (SGF). However, the effect of LA on the solubility enhancement was concentration-dependent and showed optimal enhancement at the ratio of 1:1:0.27. FTIR spectra showed the bond shifting, which confirmed the formation of new complexes. The surface morphology of complexes in SEM studies showed the rough surface of lyophilization and solvent evaporation products, while physical mixing revealed a comparatively crystalline surface. The exothermic peaks in DSC diffractograms showed diminished peaks previously observed in the diffractogram of pure drug and LA. Lyophilized ternary complexes displayed significantly enhanced thermal stability, as observed from the thermograms of TGA. In conclusion, it was observed that the preparation method and a specific drug-to-polymer and amino acid ratio are critical for achieving high drug solubility and stability. These complexes seem to be promising candidates for novel drug delivery systems development.
Modeling techniques and algorithms for probabilistic model-based diagnosis and repair

Model-based diagnosis centers on the use of a behavioral model of a system to infer diagnoses of anomalous behavior. For model-based diagnosis techniques to become practical, some serious problems in the modeling of uncertainty and in the tractability of uncertainty management have to be addressed. These questions include: How can we tractably generate diagnoses in large systems? Where do the prior probabilities of component failure come from when modeling a system? How do we tractably compute low-cost repair plans? How can we do diagnosis even if only partial descriptions of device operation are available? This dissertation seeks to bring model-based diagnosis closer to being a viable technology by addressing these problems. We address the tractability of model-based diagnosis in two complementary ways. We show how the structure of a hierarchical system specification can be exploited to yield an efficient diagnosis algorithm. We also develop polynomial-time algorithms to generate candidate diagnoses in decreasing order of likelihood. These candidates are then tested for consistency with observations. In effect, this gives us a generate-and-test scheme for enumerating diagnoses in decreasing order of plausibility. To help with modeling, we relate the prior probability of failure of a component to a temporal model of the failure process of the component. This work also gives us a principled way of modeling possible state changes in the system when performing diagnosis with multiple observations where each observation occurs at a different time. Turning to the repair problem, we develop a general-purpose algorithm for computing optimal repair plans. The algorithm makes no assumptions about the structure of the system model and hence has to exhaustively search through all possible plans. We go on to demonstrate an interesting and useful restriction on the system model which allows the optimal repair plan to be computed in polynomial time. To perform diagnosis when only partial device descriptions are available, we develop a way of "completing" the model such that repair cost estimates computed from the completed model are conservative upper bounds of the actual repair cost. We conclude with a discussion about promising extensions of this work.