Miracle-dz/UniFAR-FTUs huggingface dataset:
Combinations of Photodynamic Therapy with Other Minimally Invasive Therapeutic Technologies against Cancer and Microbial Infections

The rapid rise in research and development following the discovery of photodynamic therapy to establish novel photosensitizers and overcome the limitations of the technology soon after its clinical translation has given rise to a few significant milestones. These include several novel generations of photosensitizers, the widening of the scope of applications, leveraging of the offerings of nanotechnology for greater efficacy, selectivity for the disease over host tissue and cells, the advent of combination therapies with other similarly minimally invasive therapeutic technologies, the use of stimulus-responsive delivery and disease targeting, and greater penetration depth of the activation energy. Brought together, all these milestones have contributed to the significant enhancement of what is still arguably a novel technology. Yet the major applications of photodynamic therapy still remain firmly located in neoplasms, from where most of the new innovations appear to launch to other areas, such as microbial, fungal, viral, acne, wet age-related macular degeneration, atherosclerosis, psoriasis, environmental sanitization, pest control, and dermatology. Three main value propositions of combinations of photodynamic therapy include the synergistic and additive enhancement of efficacy, the relatively low emergence of resistance and its rapid development as a targeted and high-precision therapy. Combinations with established methods such as chemotherapy and radiotherapy and demonstrated applications in mop-up surgery promise to enhance these top three clinical tools. From published in vitro and preclinical studies, clinical trials and applications, and postclinical case studies, seven combinations with photodynamic therapy have become prominent research interests because they are potentially easily applied, showing enhanced efficacy, and are rapidly translating to the clinic. These include combinations with chemotherapy, photothermal therapy, magnetic hyperthermia, cold plasma therapy, sonodynamic therapy, immunotherapy, and radiotherapy. Photochemical internalization is a critical mechanism for some combinations.
Sex differences in coronary microvascular resistance measured by a computational fluid dynamics model

Background Increased coronary microvascular resistance (CMVR) is associated with coronary microvascular dysfunction (CMD). Although CMD is more common in women, sex-specific differences in CMVR have not been demonstrated previously. Aim To compare CMVR between men and women being investigated for chest pain. Methods and results We used a computational fluid dynamics (CFD) model of human coronary physiology to calculate absolute CMVR based on invasive coronary angiographic images and pressures in 203 coronary arteries from 144 individual patients. CMVR was significantly higher in women than men (860 [650–1,205] vs. 680 [520–865] WU, Z = −2.24, p = 0.025). None of the other major subgroup comparisons yielded any differences in CMVR. Conclusion CMVR was significantly higher in women compared with men. These sex-specific differences may help to explain the increased prevalence of CMD in women.
Simultaneous optimization of quality and reliability characteristics through designed experiment

ABSTRACT Many practical situations have both a quality characteristic and a reliability characteristic with the goal to find an appropriate compromise for the optimum conditions. Standard analyses of quality and reliability characteristics in designed experiments usually assume a completely randomized design. However, many experiments involve restrictions on randomization, e.g., subsampling, blocking, split-plot. This article considers an experiment involving both a quality characteristic and a reliability characteristic (lifetime) within a subsampling protocol. The particular experiment uses Type I censoring for the lifetime. Previous work on analyzing reliability data within a subsampling protocol assumed Type II censoring. This article extends such an analysis for Type I censoring. The method then uses a desirability function approach combined with the Pareto front to obtain a trade-off between the quality and reliability characteristics. A case study illustrates the methodology.
Model‐based Crowd Behaviours in Human‐solution Space

Realistic crowd simulation has been pursued for decades, but it still necessitates tedious human labour and a lot of trial and error. The majority of currently used crowd modelling is either empirical (model‐based) or data‐driven (model‐free). Model‐based methods cannot fit observed data precisely, whereas model‐free methods are limited by the availability/quality of data and are uninterpretable. In this paper, we aim at taking advantage of both model‐based and data‐driven approaches. In order to accomplish this, we propose a new simulation framework built on a physics‐based model that is designed to be data‐friendly. Both the general prior knowledge about crowds encoded by the physics‐based model and the specific real‐world crowd data at hand jointly influence the system dynamics. With a multi‐granularity physics‐based model, the framework combines microscopic and macroscopic motion control. Each simulation step is formulated as an energy optimization problem, where the minimizer is the desired crowd behaviour. In contrast to traditional optimization‐based methods which seek the theoretical minimizer, we designed an acceleration‐aware data‐driven scheme to compute the minimizer from real‐world data in order to achieve higher realism by parameterizing both velocity and acceleration. Experiments demonstrate that our method can produce crowd animations that are more realistically behaved in a variety of scales and scenarios when compared to the earlier methods.
Proceedings of the Forty-first Meeting of the Agricultural Research Modellers' Group

This group, which is concerned with the applications of mathematics to agricultural science, was formed in 1970 and has since met at approximately yearly intervals in London for one-day meetings. The forty-first meeting of the group, chaired by Dr Derek Rose of the School of Agriculture, Food and Rural Development, University of Newcastle upon Tyne, was held in the Kohn Centre at the Royal Society, 6–9 Carlton House Terrace, London, on Friday, 3 April 2009 when the following papers were read.
A Novel Angular-Based Unsupervised Domain Adaptation Framework for Image Classification

Domain adaptation (DA) deals with the common problem of distribution mismatch between the training and the target data. A model tested on data that comes from a different source than the training data will not perform well. DA methods aim to utilize the available training and testing data to model a target domain classifier. Domain invariant features are extracted and used to minimize the distribution divergence between the source and target domains. At the same time, there exist some imperative limitations. The angle between the subspaces of the source and target domains is not reduced during distribution alignment, and the formulation of an objective function, that reduces geometric and statistical deviations, must be used. None of the DA strategies in use today address these constraints simultaneously. Therefore, we propose a novel DA framework, called angular-based unsupervised domain adaptation framework for image classification (AUDAF) to address these limitations. AUDAF first obtains the pseudo labels for the unlabeled target domain data using a simple k-Nearest Neighbor classifier (trained on source domain data) and then formulates a robust objective function to reduce domain discrepancy. The objective function can be solved efficiently in a closed form by considering a couple of projection vector matrices (one for source, the other for target). Extensive testing on benchmark DA datasets shows that AUDAF performs better than existing DA methods in terms of classification accuracy.
Statistical inference for the Nadarajah-Haghighi distribution based on ranked set sampling with applications

In this article, the maximum likelihood and Bayes inference methods are discussed for determining the two unknown parameters and specific lifetime parameters of the Nadarajah-Haghighi distribution, such as the survival and hazard rate functions, with the inclusion of ranked set sampling and simple random sampling. The estimated confidence intervals for the two parameters and any function of them are developed based on the Fisher-information matrix. Metropolis-Hastings algorithm and Lindley-approximation are used for generating the Bayes estimates and related highest posterior density credible ranges for the unknown parameters and reliability parameters under the presumption of conjugate gamma priors. A Monte-Carlo simulation study and a real-life data set have been used to assess the efficacy of the proposed methods.
Rational approximations for solving cauchy problems

In this letter, numerical solutions of Cauchy problems are considered by multivariate Pade approximations (MPA). Multivariate Pade approximations (MPA) were applied to power series solutions of Cauchy problems that solved by using He’s variational iteration method (VIM). Then, numerical results obtained by using multivariate Pade approximations were compared with the exact solutions of Cauchy problems.
RD50 overview: development of radiation hard detectors for high luminosity colliders

RD50 (Radiation Hard Semiconductor Devices For High Luminosity Colliders) is a transversal CERN R&D collaboration across different experiments interested in pushing the radiation hardness of current silicon detectors. Activity in the collaboration is organized in 4 fronts: defect and material characterization (mapping microscopic features to macroscopic properties of devices), detector characterization (including development of new measurement techniques, like edge-TCT or Two Photon Absorption TCT), new structures (3D sensors, detectors with amplification, slim edges, . . . ) and finally full detector systems (studying the operation of detectors up to LHC readout frequencies and rates). A device simulation subgroup is in charge of understanding the performance of actual devices and projecting the endurance of these devices in the long term.
Classification of Herring, Salmon, and Bubbles in Multifrequency Echograms Using U-Net Neural Networks

Echosounders are used by fisheries and ocean observatories, but significant manual effort is required to classify species of interest within multifrequency echograms. This article investigates the use of modified U-Net convolutional neural networks for the pixel-level classification of biological and physical data in echogram images with accurate classification of herring and salmon schools, bubbles, and the sea surface. Data were collected on the coast of British Columbia, Canada, over two years using an Acoustic Zooplankton and Fish Profiler at four frequencies (67, 125, 200, 455 kHz). In addition, simulated data (water depth and solar elevation angle) provide spatial and temporal context to improve the quality of predictions. Redundancy is built into the model by using a tiling strategy during training and classification. During training, using a limited set of annotated data, translational augmentation encodes the U-Nets with robust features that enable applications for alternate deployment configurations (lower sampling rates or alternate water depths). To ensure broad applicability, these networks were trained to classify echograms with noise left intact. The best-performing model classifies herring, salmon, and bubble classes with $\rm {F_{1}}$ scores of 93.0%, 87.3%, and 86.5%, respectively. The results are accurate even when multiple classes are in close proximity, thus, retaining biological data that would otherwise be discarded due to surface bubble noise.
Self-stabilized discharge filament in plane-parallel barrier discharge configuration: formation, breakdown mechanism, and memory effects

Single self-stabilized discharge filaments were investigated in the plane-parallel electrode configuration. The barrier discharge was operated inside a gap of 3 mm shielded by glass plates to both electrodes, using helium-nitrogen mixtures and a square-wave feeding voltage at a frequency of 2 kHz. The combined application of electrical measurements, ICCD camera imaging, optical emission spectroscopy and surface charge diagnostics via the electro-optic Pockels effect allowed the correlation of the discharge development in the volume and on the dielectric surfaces. The formation criteria and existence regimes were found by systematic variation of the nitrogen admixture to helium, the total pressure and the feeding voltage amplitude. Single self-stabilized discharge filaments can be operated over a wide parameter range, foremost, by significant reduction of the voltage amplitude after the operation in the microdischarge regime. Here, the outstanding importance of the surface charge memory effect on the long-term stability was pointed out by the recalculated spatio-temporally resolved gap voltage. The optical emission revealed discharge characteristics that are partially reminiscent of both the glow-like barrier discharge and the microdischarge regime, such as a Townsend pre-phase, a fast cathode-directed ionization front during the breakdown and radially propagating surface discharges during the afterglow.
Variance estimation of modal parameters from input/output covariance-driven subspace identification

For Operational Modal Analysis (OMA), the vibration response of a structure from ambient and unknown ex-citation is measured and used to estimate the modal parameters. For OMA with eXogenous inputs (OMAX), some of the inputs are known in addition, which are considered as realizations of a stochastic process. When identifying the modal parameters from noisy measurement data, the information on their uncertainty is most relevant. Previously, a method for variance estimation has been developed for the output-only case with covariance-driven subspace identification. In this paper, a recent extension of this method for the in-put/output covariance-driven subspace algorithm is discussed. The resulting variance expressions are easy to evaluate and computationally tractable when using an efficient implementation. Based on Monte Carlo simulations, the quality of identification and the accuracy of variance estimation are evaluated. It is shown how the input information leads to better identification results and lower uncertainties.
Key predictors of psychological distress and wellbeing in Australian frontline healthcare workers during COVID-19 (Omicron wave)

Introduction The COVID-19 pandemic has led to significant challenges for frontline healthcare workers’ (FHW), raising many mental health and wellbeing concerns for this cohort. To facilitate identification of risk and protective factors to inform treatment and interventions, this study investigated key predictors of psychological distress and subjective wellbeing in FHWs. Methods During the Omicron wave of the COVID-19 pandemic (January 2022), Victorian (Australia) doctors, nurses, allied health and non-medical staff from Emergency Departments, Intensive Care units, Aged Care, Hospital In The Home, and COVID Wards completed a cross-sectional survey consisting of the Kessler 6 item (Psychological Distress), Personal Wellbeing Index (Subjective Wellbeing), Coronavirus Health Impact Survey tool (COVID-19 related factors) and occupational factors. Multivariable linear regressions were used to evaluate unadjusted and adjusted associations. Relative weight analysis was used to compare and identify key predictors. Results Out of 167 participants, 18.1% screened positive for a probable mental illness and a further 15.3% screened positive for low wellbeing. Key risk factors for greater psychological distress included COVID infection worries, relationship stress and younger age. For both psychological distress and lower wellbeing, health status and supervisor support were key protective factors, while infection risks were key risk factors. Only positive changes in relationship quality was protective of lower wellbeing. Conclusion This study highlights the significance of social determinants and individual level factors alongside work related factors, in influencing FHWs’ mental health and wellbeing during public health crises, such as the COVID-19 pandemic. Findings suggest that future interventions and supports should take a more holistic approach that considers work, social and individual level factors when supporting FHWs’ mental health and wellbeing.
Deep Learning Based Apples Counting for Yield Forecast Using Proposed Flying Robotic System

Nowadays, Convolution Neural Network (CNN) based deep learning methods are widely used in detecting and classifying fruits from faults, color and size characteristics. In this study, two different neural network model estimators are employed to detect apples using the Single-Shot Multibox Detection (SSD) Mobilenet and Faster Region-CNN (Faster R-CNN) model architectures, with the custom dataset generated from the red apple species. Each neural network model is trained with created dataset using 4000 apple images. With the trained model, apples are detected and counted autonomously using the developed Flying Robotic System (FRS) in a commercially produced apple orchard. In this way, it is aimed that producers make accurate yield forecasts before commercial agreements. In this paper, SSD-Mobilenet and Faster R-CNN architecture models trained with COCO datasets referenced in many studies, and SSD-Mobilenet and Faster R-CNN models trained with a learning rate ranging from 0.015–0.04 using the custom dataset are compared experimentally in terms of performance. In the experiments implemented, it is observed that the accuracy rates of the proposed models increased to the level of 93%. Consequently, it has been observed that the Faster R-CNN model, which is developed, makes extremely successful determinations by lowering the loss value below 0.1.
The role of glucocorticoids in increasing cardiovascular risk

Introduction Different studies provide conflicting evidence regarding the potential for glucocorticoids (GCs) to increase the risk of cardiovascular diseases. This study performed a systematic review and meta-analysis to determine the correlation between GCs and cardiovascular risk, including major adverse cardiovascular events (MACE), death from any cause, coronary heart disease (CHD), heart failure (HF), and stroke. Methods We performed a comprehensive search in PubMed and Embase (from inception to June 1, 2022). Studies that reported relative risk (RR) estimates with 95% confidence intervals (CIs) for the associations of interest were included. Results A total of 43 studies with 15,572,512 subjects were included. Patients taking GCs had a higher risk of MACE (RR = 1.27, 95% CI: 1.15–1.40), CHD (RR = 1.25, 95% CI: 1.11–1.41), and HF (RR = 1.92, 95% CI: 1.51–2.45). The MACE risk increased by 10% (95% CI: 6%–15%) for each additional gram of GCs cumulative dose or by 63% (95% CI: 46%–83%) for an additional 10 μg daily dose. The subgroup analysis suggested that not inhaled GCs and current GCs use were associated with increasing MACE risk. Similarly, GCs were linked to an increase in absolute MACE risk of 13.94 (95% CI: 10.29–17.58) cases per 1,000 person-years. Conclusions Administration of GCs is possibly related with increased risk for MACE, CHD, and HF but not increased all-cause death or stroke. Furthermore, it seems that the risk of MACE increased with increasing cumulative or daily dose of GCs.
Resonant Magnetic Scattering Studies using Synchrotron Radiationand Laser-Generated Extreme Ultraviolet Light

In this thesis magnetic domain patterns of ferromagnetic alloys are studied using resonant magnetic scattering (RMS). For this purpose synchrotron radiation in the soft X-ray range and laser-based extreme ultraviolet (XUV) light from a laboratory light source are employed. The synchrotron measurements give detailed information about the properties of magnetic domains with nanometer precision and element selectivity. These specialties allow investigations of domain patterns of complex layered systems, including FePd/CoPd bilayers or CoPd/Pd/NiFe trilayers. Time-resolved experiments with visible laser radiation are conducted to examine the laserinduced demagnetization of FePd/CoPd bilayers, pointing out the influence of intense laser pulses on the magnetic coupling of the two layers. This technique is capable of tracing the temporal evolution of the magnetization, nevertheless it cannot image nanometer-sized magnetic domains due to the limited spatial resolution. Consequently, a new approach is necessary to resolve domain patterns and ultrafast magnetization dynamics at the same time. This aim motivates the most important question addressed in this thesis: whether a tabletop XUV light source based on high-order harmonic generation (HHG) can be used for RMS experiments on magnetic domain patterns. In this context it is further shown, how intense laser exposure causes permanent and reversible modifications of the magnetic nanostructure and the sample morphology. Finally, coherence properties of the generated XUV radiation are examined, using nonmagnetic scattering at grains and knife-edge diffraction. The results obtained reveal the advantages and limitations of HHG sources for applications which demand a high light coherence.
The views of high school students about proof and their levels of proof (The case of Trabzon)

Abstract This study was conducted to determine the views of high school students about proof and these students’ levels of proof. Case study method was used in this descriptive study. The data of the study were obtained by conducting a questionnaire which consists of eight open-ended questions to total 125 10 th grade students studying in two different secondary schools in Trabzon during 2006-2007 school year. The views of the students regarding proof were coded and their levels of proof were analyzed based on the classification of Miyazaki related to proof. At the end of the study, high school students’ competencies of doing proof were found below the desired level, as well as their use of different proof types. Based on these results, it’s recommendedto give room in the classes to activities that would improve students’ mathematical thinking skills and increase their levels ofproof. © 2010 Elsevier Ltd. All rights reserved. Keywords: Mathematics education; mathematical proof; mathematical reasoning; proof levels; student views
Using Theraplay® to support children and families: a scoping review

ABSTRACT Theraplay® is an attachment-based intervention involving adults and children which is practised in a variety of settings across the world. The aim of this scoping review is to explore how Theraplay® has been used as an intervention with children and families in order to inform future practice and to identify any gaps in the current literature. By utilizing a systematic search of key databases and grey literature, eleven papers met the inclusion criteria. Findings showed that Theraplay® has been delivered as an intervention to children and families with a variety of presenting difficulties and in some cases has been used alongside other therapeutic interventions to make a positive change. The review highlights a diversity of methods and measures for the evaluation of outcomes of Theraplay®. Recommendations for further research are suggested.