Miracle-dz/UniFAR-FTUs huggingface dataset:
Bootstrapping stochastic regression models under homoskedasticity: wild bootstrap vs. pairs bootstrap

We investigate by simulation how the wild bootstrap and pairs bootstrap perform in t and F tests of regression parameters in the stochastic regression model, where explanatory variables are stochastic and not given and there exists no heteroskedasticity. The wild bootstrap procedure due to Davidson and Flachaire [The wild bootstrap, tamed at last, Working paper, IER#1000, Queen's University, 2001] with restricted residuals works best but its dominance is not strong compared to the result of Flachaire [Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap, Comput. Statist. Data Anal. 49 (2005), pp. 361–376] in the fixed regression model where explanatory variables are fixed and there exists heteroskedasticity.
The role of dispersion in ultrafast optics

In this article, we review the phenomenon of dispersion, paying particular attention to its impact in the optics of ultrashort pulses, as well as its measurement and management. At present, lasers generating coherent bandwidths of several hundred nanometers have been demonstrated and correspondingly short pulses of 10 fs or so are quite usual. The limits to the breadth of optical spectra and brevity of pulse durations that may be achieved are often set by the dispersive properties of the linear optical elements of which the source is constructed. Progress in ultrafast optics to date has therefore relied extensively on the development of ways to characterize and manipulate dispersion. The means by which this can be accomplished are significantly different for laser oscillators and laser amplifiers, as well as for nonlinear interactions that are used to extend the range of frequencies at which short optical pulses are available, but in all cases it is this phenomenon that determines the output of current op.
LOCAL ASPECTS OF THE ADSORBATE-SUBSTRATE CHEMICAL BOND IN N/CU(100) AND O/CU(100)

Both the occupied and unoccupied adsorbate valence states have been probed by x-ray emission and x-ray absorption spectroscopies for N/Cu(100) and the low coverage phase of O/Cu(100). By carrying out experiments using two different measurement geometries, the adsorbate out-of-plane states (2p{sub z}) have been separated from the in-plane states (2p{sub xy}). The experimental results are compared to the partial density of states (DOS) obtained from full-potential linear muffin-tin orbital calculations, with good agreement in the case ofN/Cu(100). This agreement confirms that it is possible to interpret the experimental data in terms of the adsorbate partial DOS. Our combined experimental and theoretical study also provides a detailed understanding of the local chemical bond for N and O adsorbed on Cu(100). {copyright} {ital 1998} {ital The American Physical Society}
A numerical analysis of ZnTe/AZO as tunnel junction in CdTe/Si tandem solar cell

Recently, interest has shifted towards developing multijunction or tandem solar cells due to their high potential to generate higher efficiency than traditional single-junction solar cells. Cadmium telluride (CdTe) and silicon (Si) solar cell materials have demonstrated significant potential in photovoltaic energy generation as tandem structures if fully developed. One approach for optimising CdTe/Si is to develop an effective tunnel junction that can electrically and optically interconnect the cadmium telluride and silicon cells with minimal loss. The wxAMPS 3.0 numerical simulation was used in this work to develop CdTe/Si tandem using zinc telluride/aluminium doped zinc oxide (ZnTe/AZO) as a tunnel junction (TJ). The result obtained shows an optimum efficiency of over 36 % with Voc = 1.945 V, Jsc = 21.519 mA/cm2, and FF = 86.823 % utilising the optimal 200 nm CdTe and Si absorber thickness of 300 μm. An analysis of the demonstrated results suggests that ZnTe/AZO tunnel junction will significantly contribute to the realisation of the CdTe/Si tandem solar cell. Hence, upon inserting a 40 nm highly doped ZnTe/AZO tunnelling junction to a CdTe/Si tandem configuration, the solar cell’s performance was enhanced by 48.190%.
Effects of an innovative daily physical activity intervention on the health and fitness status of primary school children: a cluster randomized controlled trial

Aims An important barrier for a nationwide implementation of a daily physical activity (PA) intervention at primary schools is the lack of spatial and human resources. Therefore, the aim was to develop an innovative PA intervention that can be implemented, without additional spatial and human resource requirements. The effects of this intervention on anthropometrics and health-related fitness of primary school children were investigated. Methods In twelve primary schools, 24 classes were randomly allocated to an intervention and control group. In the intervention group, children received mix physical education lessons, cognitive content using movement, and additional opportunities for extracurricular physical activities (home exercises) over a 9-month period. In the control group, children received the usual physical education classes. At baseline (Sept 2021) and at follow-up immediately after the intervention (June 2022), body weight, height, waist circumference and fitness parameters (cardiorespiratory fitness, muscle strength, flexibility) were measured. Intervention effects were assessed using multilevel regression models, adjusted for gender, urban/rural school, sports club membership, and for baseline values of the outcome parameter. Interactions between intervention and being a member of a sports club were assessed. Results Of 485 invited children, 412 (85%) were included in the analyses; 228 in the intervention and 184 in the control group. Children were 9.7 (SD 0.5) years old at baseline. In the total intervention group, a reduction in waist-to-height ratio and increase in all fitness parameters were found compared to the control group. Interactions with being a member of a sports club were found, and intervention effects were more pronounced in the group of children who were not a member of a sports club. Conclusions Our daily physical activity intervention for primary schools showed positive effects on important health related parameters, especially for children who were not in a sports club and had most of their PA at school. Wider implementation of this intervention in primary schools is warranted, which should be feasible, since we made sure that no extra resources are needed. Trial registration DRKS00025515.
Action-sequence learning, habits and automaticity in obsessive-compulsive disorder

Enhanced habit formation, greater automaticity and impaired goal/habit arbitration in obsessive-compulsive disorder (OCD) are key hypotheses from the goal/habit imbalance theory of compulsion which have not been directly investigated. This article tests these hypotheses using a combination of newly developed behavioral tasks. First, we trained patients with OCD and healthy controls, using a novel smartphone app, to perform chunked action sequences, previously shown to engage habit brain circuitry. The motor training was daily over one month period. There was equivalent procedural learning and attainment of habitual performance (measured with an objective criteria of automaticity) in both groups, despite greater subjective habitual tendencies in patients with OCD, self-reported via a recently developed questionnaire. We then used a combination of follow-up behavioral tasks to further assess the arbitration between previous automatic and new goal-directed action sequences. We found no evidence for impairments of goal/habit arbitration in OCD following re-evaluation based on monetary feedback, although there was a greater preference for engaging in the trained habitual sequence under certain conditions which may have derived from its intrinsic value. These findings may lead to a reformulation of the goal/habit imbalance hypothesis in OCD. Finally, OCD patients with higher compulsivity scores and habitual tendencies showed more engagement with the motor habit-training app and reported symptom alleviation, with implications for its potential use as a form of habit reversal therapy.
Prediction of Bubble Behavior in Subcooled Pool Boiling Based on Microlayer Model

Bubble behaviors of subcooled nucleate pool boiling are investigated both experimentally and theoretically. The analytical study based oil the dynamic microlayer model predicts that one cycle of an individual bubble experiences four stages, i.e., the initial growth with a semi-spherical shape, the final growth with a spherical segment geometry, the condensation process and the waiting time. Also, vapor bubbles on the Pt wire are experimentally observed by a high-speed camera at the speed of 10 000 frames/sec in the nucleate pool boiling of subcooled water. The predicted four stages of an individual bubble are clarified experimentally. Relatively good agreement is shown between the present predictions of the total periods of individual bubbles and experimental data.
The generalized Pareto distribution applied to rainfall depths.

The Generalized Pareto distribution is fitted to Dutch peaks-over-threshold (POT) rainfall series. Maximum likelihood (ML) estimation of the parameters is discussed, with particular reference to bias reduction for the scale and shape parameter. The improvement of the fit in the right tail by left censoring of POT series is discussed, and the dependence of the shape parameter θ of the fitted Generalized Pareto distribution on (a) mean annual number of peaks, and (b) rainfall duration, with fixed or moving starting point, is quantified.
Variational image inpainting

Inpainting is an image interpolation problem with broad applications in image and vision analysis. Described in the current expository paper are our recent efforts in developing universal inpainting models based on the Bayesian and variational principles. Discussed in detail are several variational inpainting models built upon geometric image models, the associated Euler‐Lagrange PDEs and their geometric and dynamic interpretations, as well as effective computational approaches. Novel efforts are then made to further extend this systematic variational framework to the inpainting of oscillatory textures, interpolation of missing wavelet coefficients as in the wireless transmission of JPEG2000 images, as well as light‐adapted inpainting schemes motivated by Weber's law in visual perception. All these efforts lead to the conclusion that unlike many familiar image processors such as denoising, segmentation, and compression, the performance of a variational/Bayesian inpainting scheme much more crucially depends on whether the image prior model well resolves the spatial coupling (or geometric correlation) of image features. As a highlight, we show that the Besov image models appear to be less interesting for image inpainting in the wavelet domain, highly contrary to their significant roles in thresholding‐based denoising and compression. Thus geometry is the single most important keyword throughout this paper. © 2005 Wiley Periodicals, Inc.
Nano Sensing for Early Diagnosis of Pancreatic Cancer

Pancreatic Cancer is becoming the second leading cause of cancer deaths, mainly attributed to late diagnosis. Surgical resection remains the only plausible treatment for curing patients of this cancer, but this is only possible when the cancer is localized making early detection vital. Currently, the most common early diagnostic method is the tri-phasic pancreatic-protocol CT scan. This method however has a low accuracy and specificity making it an ineffective early diagnostic. This purpose of this research was to develop a non-invasive, fast, and highly sensitive early diagnostic method for pancreatic cancer through the applications of nanotechnology. Surface Enhanced Raman Spectroscopy (SERS) is an innovative nano sensing method which results from plasmonic effect through molecules interacting with the surface of metal nanoparticles. This is a new method for trace biomarker detections and shows great potential to be an early nano-diagnostic method for Pancreatic cancer. This project uses Leucine as a biomarker for the demonstration of SERS for early pancreatic cancer detection. This is because recent studies show that Leucine has linked its overexpression with pancreatic cancer; studies have found a specificity of 100% with the use of Leucine and early diagnosis 2 to 5 years earlier than current diagnostic methods for pancreatic cancer. The parameters for SERS enhancement were optimized for the detection of Leucine using 20 nm Ag NPs. Predictive curves with linear fits were generated from an analysis of feature peaks giving the highest sensitivity for trace concentrations. This new finding shows the promising development of an early diagnostic method that is non-invasive, efficient and highly accurate for pancreatic cancer through SERS nano sensing. Keywords: Pancreatic cancer, SERS, Nanotechnology, Leucine, Early Diagnosis
Application of 3D-bioprinted nanocellulose and cellulose derivative-based bio-inks in bone and cartilage tissue engineering

212Three-dimensional (3D) printing is a modern, computer-aided, design-based technology that allows the layer-by-layer deposition of 3D structures. Bioprinting, a 3D printing technology, has attracted increasing attention because of its capacity to produce scaffolds for living cells with extreme precision. Along with the rapid development of 3D bioprinting technology, the innovation of bio-inks, which is recognized as the most challenging aspect of this technology, has demonstrated tremendous promise for tissue engineering and regenerative medicine. Cellulose is the most abundant polymer in nature. Various forms of cellulose, nanocellulose, and cellulose derivatives, including cellulose ethers and cellulose esters, are common bioprintable materials used to develop bio-inks in recent years, owing to their biocompatibility, biodegradability, low cost, and printability. Although various cellulose-based bio-inks have been investigated, the potential applications of nanocellulose and cellulose derivative-based bio-inks have not been fully explored. This review focuses on the physicochemical properties of nanocellulose and cellulose derivatives as well as the recent advances in bio-ink design for 3D bioprinting of bone and cartilage. In addition, the current advantages and disadvantages of these bio-inks and their prospects in 3D printing-based tissue engineering are comprehensively discussed. We hope to offer helpful information for the logical design of innovative cellulose-based materials for use in this sector in the future.
The Bothriolepis (Placodermi, Antiarcha) material from the Valentia Slate Formation of the Iveragh Peninsula (middle Givetian, Ireland): Morphology, evolutionary and systematic considerations, phylogenetic and palaeogeographic implications

Material of the antiarch placoderm Bothriolepis from the middle Givetian of the Valentia Slate Formation in Iveragh Peninsula, Ireland, is described and attributed to a new species, B. dairbhrensis sp. nov. A revision of the genus Bothriolepis is proposed, and its taxonomic content and previous phylogenetic analyses are reviewed, as well as the validity of morphologic characteristics considered important for the establishment of the genus, such as the shape of the preorbital recess of the neurocranium. A series of computerised phylogenetic analyses was performed, which reveals that our new species is the sister taxon to the Frasnian Scottish form B. gigantea. New phylogenetic and biogeographic analyses of the genus Bothriolepis together with comparisons between faunal assemblages reveal a first northward dispersal wave from Gondwana to Euramerica at the latest in the mid Givetian. Other Euramerican species of Bothriolepis seem to belong to later dispersal waves from Gondwana, non-excluding southward waves from Euramerica. Questions remain open such as the taxonomic validity and stratigraphic constraints for the most ancient forms of Bothriolepis in China, and around the highly speciose nature of the genus.
Racial Disparities in Quantitative MRI for African American and White Men with Prostate Cancer

The risk of prostate cancer (PCa) is strongly influenced by race and ethnicity. The purpose of this study is to investigate differences in the diagnostic performance of multiparametric MRI (mpMRI) in African American (AA) and white (W) men. 111 patients (37 AA and 74 W men) were selected from the study’s initial cohort of 885 patients after matching age, prostate-specific antigen, and prostate volume. The diagnostic performance of mpMRI was assessed using detection rates (DRs) and positive predictive values (PPVs) with/without combining Ktrans (volume transfer constant) stratified by prostate zones for AA and W sub-cohorts. The DRs of mpMRI for clinically significant PCa (csPCa) lesions in AA and W sub-cohort with PI-RADS scores ≥ 3 were 67.3% vs. 80.3% in the transition zone (TZ; p=0.026) and 81.2% vs. 76.1% in the peripheral zone (PZ; p>0.9). The Ktrans of csPCa in AA men was significantly higher than in W men (0.23±0.08 min−1 vs. 0.19±0.07 min−1; p=0.022). This emphasizes that there are race-related differences in the performance of mpMRI and quantitative MRI measures that are not reflected in age, PSA, and prostate volume.
A Robust Cooperative Coevolutionary Particle Swarm Optimization Algorithm for Triangulation of Bayesian Networks

In this paper, a cooperative coevoluationary particle swarm optimization algorithm, CCMDPSO, is proposed to solve the optimization problem of triangulation of Bayesian networks. It arranges all the variables of a given Bayesian network into some groups according to the global best solution and performs optimization on these small-scale groups. The basic optimizer of CCMDPSO is an improved discrete particle swarm optimization algorithm, MDPSO. Experiments show that CCMDPSO is an effective and robust method for the triangulation problem.
Markov-Modulated Nonhomogeneous Poisson Processes for Modeling Detections in Surveys of Marine Mammal Abundance

We consider Markov-modulated nonhomogeneous Poisson processes for modeling sightings of marine mammals in shipboard or aerial surveys. In such surveys, detection of an animal is possible only when it surfaces, and with some species a substantial proportion of animals is missed because they are diving and thus not available for detection. This needs to be adequately accounted for to avoid biased abundance estimates. The tendency of surfacing events of marine mammals to occur in clusters motivates consideration of the flexible class of Markov-modulated Poisson processes in this context. We embed these models in distance sampling models, introducing nonhomogeneity in the process to account for the fact that the observer's probability of detecting an animal decreases with increasing distance to the animal. We derive approximate expressions for the likelihood of Markov-modulated nonhomogeneous Poisson processes that enable us to estimate the model parameters through numerical maximum likelihood. The performance of the approach is investigated in an extensive simulation study, and applications to pilot and beaked whale tag data as well as to minke whale tag and survey data demonstrate its relevance in abundance estimation.
Repetitive pellet fuelling for high-density/steady-state operation on LHD

A repetitive pellet injector has been developed and pellet refuelling experiments have been launched on the Large Helical Device (LHD) in a new regime characterized by high density steady state operation. In order to suppress the disturbance to the core plasma as much as possible, we employ relatively small and slow pellets. The size and velocity of the pellets are typically 2.5 mm in diameter and 250 m s−1, respectively. The pellets were injected into neutral beam heated plasmas with repetition frequency of 10 Hz. The density rise per pellet is 1.5 × 1019 m−3 and the pellet penetration depth, which is normalized by the plasma minor radius, is typically less than 0.5. Quasi-steady-state operation was achieved at plasma parameters of , Te = Ti = 1.0 keV. Repetitive pellet fuelling showed improved energy confinement compared with massive gas puff fuelling in the high density regime in common with previous transient pellet injection experiments, and it can sustain such an improved confinement property.
Optimal filtering for incompletely measured polynomial states with multiplicative noise

In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the observation equation is introduced to reduce the original problem to the previously solved one with an invertible observation matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of linear and bilinear state equations. In the example, performance of the designed optimal filter is verified against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.
The effect of integrating knowledge‐based planning with multicriteria optimization in treatment planning for prostate SBRT

Abstract Knowledge‐based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ‐at‐risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two‐phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten‐patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non‐linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.