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"The Art and Science of Data Analysis: Unraveling Insights for Effective Decision Making" explores the interdisciplinary nature of data analysis, combining both scientific methodologies and creative approaches to extract valuable insights. This paper highlights the significance of integrating analytical techniques with intuitive thinking to enable effective decision-making processes. By unraveling the complexities of data analysis, organizations can leverage insights to drive informed decisions and achieve desired outcomes.

Magnetic resonance imaging (MRI) has revolutionized diagnostic imaging in medicine, providingdetailed anatomical and functional information without the use of ionizing radiation. Over theyears, significant advancements in MRI technology have further enhanced its capabilities,allowing for improved image quality, faster scanning times, and expanded clinical applications.This article aims to explore the recent advancements in MRI, their impact on diagnostic imaging,and their role in advancing precision medicine.

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. Thesuccess of liver transplantation depends not only on the surgical expertise but also on theappropriate selection of suitable liver donors. This article provides a comprehensive overview ofthe systems-based approach to liver donor selection, focusing on optimizing organ allocation forthe best possible outcomes. It discusses the evaluation criteria, organ allocation systems, andconsiderations for donor-recipient matching.

Computed Tomography (CT) scan is a widely used imaging modality that plays a crucial role inmodern radiology. It provides detailed cross-sectional images of the body, enabling physicians todiagnose various conditions, plan treatments, and monitor therapeutic interventions. This articleexplores the diverse applications of CT scan in modern radiology, highlighting its role in diagnosis,treatment planning, and patient management.

Emerging Trends in MRI and CT Scan: Innovations in ImagingTechnology for Accurate Diagnosis

We study the continuity equation of La Nave-Tian, extended to the Hermitian setting by Sherman-Weinkove, on Hopf and Inoue surfaces. We prove a priori estimates for solutions in both cases, and Gromov-Hausdorff convergence of Inoue surfaces to a circle., 15 pages

In this paper, we propose a coupled system of complex Hessian equations which generalizes the equation for constant scalar curvature K\"ahler (cscK) metrics. We show this system can be realized variationally as the Euler-Lagrange equation of a Hessian version of the Mabuchi K-energy in an infinite dimensional space of $k$-Hessian potentials, which can be seen as an infinite dimensional Riemannian manifold with negative sectional curvature. Finally, we prove an a priori $C^0$-estimate for this system which depends on the Entropy, which generalizes a fundamental result of Chen and Cheng for cscK metrics., Comment: 13 pages

Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks. Over the last years, vision transformers (ViTs) have appeared as a competitive alternative to CNNs, yielding impressive levels of performance in the natural image domain, while possessing several interesting properties that could prove beneficial for medical imaging tasks. In this work, we explore the benefits and drawbacks of transformer-based models for medical image classification. We conduct a series of experiments on several standard 2D medical image benchmark datasets and tasks. Our findings show that, while CNNs perform better if trained from scratch, off-the-shelf vision transformers can perform on par with CNNs when pretrained on ImageNet, both in a supervised and self-supervised setting, rendering them as a viable alternative to CNNs., Comment: Extended version of arXiv:2108.09038 originally published at the ICCV 2021 Workshop on Computer Vision for Automated Medical Diagnosis

Many models of intuitive physical reasoning posit some kind of mental simulation mechanism, yet everyday environments frequently contain far more objects than people could plau- sibly represent with their limited cognitive capacity. What determines which objects are actually included in our repre- sentations? We asked participants to predict how a ball will bounce through a complex field of obstacles, and probed work- ing memory for objects in the scene that were more and less likely to be relevant to the ball’s trajectory. We evaluate differ- ent accounts of relevance and find that successful object mem- ory is best predicted by how frequently a ball’s trajectory is expected to contact that object under a probabilistic simulation model. This suggests that people construct representations for mental simulation efficiently and dynamically, on the fly, by adding objects “just in time”: only when they are expected to become relevant for the next stage of simulation.

If we want to know whether a stack of blocks will fall, we might form our judgment by using mental simulation or simple heuristics. How do we know which strategy to use? Here, we test whether people’s strategy selection is “resource rational,” balancing their utilities and costs. We had participants judge how a balance beam will fall – a task that relies on both simulation and heuristics – and imposed visuospatial cognitive load to increase the cost of mental simulation, expecting the use of simulation to then decrease and the use of heuristics to increase. When cognitive load varied between participants, higher load led to less simulation. However, when load varied across trials for an individual participant, they instead used simulation at about the same rate as high-load-only participants. Thus strategy selection is sensitive to cognitive costs but it is also sticky, not accounting for rapidly fluctuating costs.

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Materials

This protocol was developed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Extension for Scoping Reviews: Checklist and Explanation http://www.prisma-statement.org/Extensions/ScopingReviews. This protocol will be registered prospectively with the Open Science Framework. This scoping review aims to: Map the extent and level of evidence that exists specific to the effectiveness of surfing’s influence on symptoms of mental health. Identify the limitations of the current evidence specific to the effectiveness of surfing’s influence on symptoms of mental health, and to provide recommendations for future research on the topic.

Methods and Measures

Description of esstential elements

Design and Analysis

Experimental Procedure

A scoping review which aims to identify and explore scientific literature pertaining to surfing equipment design. This review looks to identify themes and knowledge gaps within the sport of surfing.

This scoping review was conducted to provide a comprehensive overview of the current literature relevant to aquatic exercise and its effect on mental health and overall well-being amongst a wide range of populations. It also aims to a) clarify the intervention type, volume, measurement tools used, and populations best served in relation to mental health improvements and b) to identify any gaps in the literature so recommendations can be made for future research.

This scoping review seeks to assess and analyse the body of scientific literature specific to wrist worn fitness trackers to measure cardiovascular parameters.

This scoping review aims to identify physiological, musculoskeletal and anthropometric characteristics that differentiate competitive surfers from recreational surfers. Secondly, it also aims to outline the most appropriate battery of physiological and musculoskeletal tests that could be used to evaluate training interventions and/or athlete screening.

The primary objectives of this scoping review were to (1) summarise the volume and type of scientific literature specific to skin cancer and sun protective measures in water-based sports, (2) examine the current levels of compliance and adherence to sun protective behaviours in water-based sports and (3) identify any knowledge gaps to inform future research or skin health guidelines.

This project investigates the influence of trait anxiety,depression and autism on Theory of Mind. We intend to investigate 1) how traits influence the type of errors that are being made when evaluating social situations 2) what visual information people are focusing on when they are making these errors.

R and ggplot2 are tools for making impactful data visualizations. Some may fear the “code” learning curve, but with ggplot2’s “grammar of graphics” the syntax becomes intuitive and builds on itself. This workshop aims to introduce beginners to practical hands-on examples that can be applied to one’s own data analyses. Experienced users will have the opportunity to test their knowledge and learn new tricks and technique. Improve your research exploration and communication skills.

In this project, we present a computational model that explains when and why effects like the conjunction fallacy are compatible with simulation accounts of physical reasoning.

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may be using principled approximations, similar to models developed by engineers for the purposes of real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the "body" representation), as opposed to fine-grained forms for recognition (the "shape" representation). We used three classic psychophysical tasks (causality perception, collision detection, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on approximate bodies for physical reasoning, and that this approximation lies between convex hulls and fine-grained shapes.

This scoping review is aiming to explore the predominate modes of physical activity (PA) within regions of the highest concentrations of centenarians living past the age of 100 years. Knowledge gaps within certain regions will be addressed and the modes of PA that lead to longevous, independent, and robust lives will be uncovered.

A scoping review on the use of topical and oral magnesium in the alleviation of acute musculoskeletal pain

The Electron Ion Collider (EIC) under construction at Brookhaven National Laboratory is a high luminosity collider as the next major research facility for the nuclear physics community. Among the numerous RF subsystems in the EIC, the electron storage ring (ESR) fundamental RF cavities system is one of the most challenging. This system will handle a high beam current of up to 2.5 A and replenish up to 10 MW of beam power losses from synchrotron radiation and HOM. Variable coupling is required in the cavities due to the wide range of required total RF voltage and beam current combinations. In this paper, we will present the status of the design and future plans., Proceedings of the 13th International Particle Accelerator Conference, IPAC2022, Bangkok, Thailand

Dynamical systems that are contracting on a subspace are said to be semicontracting. Semicontraction theory is a useful tool in the study of consensus algorithms and dynamical flow systems such as Markov chains. To develop a comprehensive theory of semicontracting systems, we investigate seminorms on vector spaces and define two canonical notions: projection and distance semi-norms. We show that the well-known lp ergodic coefficients are induced matrix seminorms and play a central role in stability problems. In particular, we formulate a duality theorem that explains why the Markov-Dobrushin coefficient is the rate of contraction for both averaging and conservation flows in discrete time. Moreover, we obtain parallel results for induced matrix log seminorms. Finally, we propose comprehensive theorems for strong semicontractivity of linear and non-linear time-varying dynamical systems with invariance and conservation properties both in discrete and continuous time.

Property Specification Language (PSL) is a form of temporal logic that has been mainly used in discrete domains (e.g. formal hardware verification). In this paper, we show that by merging machine learning techniques with PSL monitors, we can extend PSL to work on continuous domains. We apply this technique in machine learning-based anomaly detection to analyze scenarios of real-time streaming events from continuous variables in order to detect abnormal behaviors of a system. By using machine learning with formal models, we leverage the strengths of both machine learning methods and formal semantics of time. On one hand, machine learning techniques can produce distributions on continuous variables, where abnormalities can be captured as deviations from the distributions. On the other hand, formal methods can characterize discrete temporal behaviors and relations that cannot be easily learned by machine learning techniques. Interestingly, the anomalies detected by machine learning and the underlying time representation used are discrete events. We implemented a temporal monitoring package (TEF) that operates in conjunction with normal data science packages for anomaly detection machine learning systems, and we show that TEF can be used to perform accurate interpretation of temporal correlation between events., Comment: 7 pages, 16 figures

The practical benefits of hybrid quantum information processing hardware that contains continuous-variable objects (bosonic modes such as mechanical or electromagnetic oscillators) in addition to traditional (discrete-variable) qubits have recently been demonstrated by experiments with bosonic codes that reach the break-even point for quantum error correction and by efficient Gaussian boson sampling simulation of the Franck-Condon spectra of triatomic molecules that is well beyond the capabilities of current qubit-only hardware. The goal of this Co-design Center for Quantum Advantage (C2QA) project is to develop an instruction set architecture (ISA) for hybrid qubit/bosonic mode systems that contains an inventory of the fundamental operations and measurements that are possible in such hardware. The corresponding abstract machine model (AMM) would also contain a description of the appropriate error models associated with the gates, measurements and time evolution of the hardware. This information has been implemented as an extension of Qiskit. Qiskit is an opensource software development toolkit (SDK) for simulating the quantum state of a quantum circuit on a system with Python 3.7+ and for running the same circuits on prototype hardware within the IBM Quantum Lab. We introduce the Bosonic Qiskit software to enable the simulation of hybrid qubit/bosonic systems using the existing Qiskit software development kit. This implementation can be used for simulating new hybrid systems, verifying proposed physical systems, and modeling systems larger than can currently be constructed. We also cover tutorials and example use cases included within the software to study Jaynes- Cummings models, bosonic Hubbard models, plotting Wigner functions and animations, and calculating maximum likelihood estimations using Wigner functions.

Additional figures and mechanical tests that assist with explaining models

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods have shown great success on natural images, and offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which are caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a self-supervised approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, leading to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks -- such as distinguishing treatments and mechanism of action.

Additional figures and mechanical tests that assist with explaining models

It is well-known that upper bounds for moments of the Riemann zeta function $\zeta(s)$ have implications for subconvexity bounds. In this paper we explore some implications in the opposite direction using functional analysis in the right-half of the critical strip. The main results characterise potential transitions in the behaviour of the moments., Comment: 17 pages

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

Supplemental Material for The Mediating Role of Internalizing Problems Between Peer Victimization and Dating Violence Victimization: A Test of the Stress Generation Hypothesis by Keith L. Kaufman, David S. Lee, Jeffrey J. Milroy, Anita Raj, Kevin Smith, Martine Hébert, Mara Brendgen and Martin Blais in Journal of Interpersonal Violence