Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of helium plasma jets emerged into ambient air: Influence of applied voltage, jet radius, and helium flow velocity on plasma jet characteristics

Simulation of guided positive streamers propagating along helium jets emerged into ambient air is performed, in the framework of a standard two-dimensional streamer model, for various values of parameters (applied voltage, helium flow velocity, and jet radius) governing the streamer dynamics and structure. Obtained dependencies of the streamer velocity, radius, and propagation length on the governing parameters are compared with available results of observations.
Effectiveness of robust optimization against geometric uncertainties in TomoHelical planning for prostate cancer.

BACKGROUND Geometrical uncertainties in patients can severely affect the quality of radiotherapy. PURPOSE We evaluated the dosimetric efficacy of robust optimization for helical intensity-modulated radiotherapy (IMRT) planning in the presence of patient setup uncertainty and anatomical changes. METHODS Two helical IMRT plans for 10 patients with localized prostate cancer were created using either minimax robust optimization (robust plan) or a conventional planning target volume (PTV) margin approach (PTV plan). Plan robustness was evaluated by creating perturbed dose plans with setup uncertainty from isocenter shifts and anatomical changes due to organ variation. The magnitudes of the geometrical uncertainties were based on the patient setup uncertainty considered during robust optimization, which was identical to the PTV margin. The homogeneity index, and target coverage (TC, defined as the V100% of the clinical target volume), and organs at risk (OAR; rectum and bladder) doses were analyzed for all nominal and perturbed plans. A statistical t-test was performed to evaluate the differences between the robust and PTV plans. RESULTS Comparison of the nominal plans showed that the robust plans had lower OAR doses and a worse homogeneity index and TC than the PTV plans. The evaluations of robustness that considered setup errors more than the PTV margin demonstrated that the worst-case perturbed scenarios for robust plans had significantly higher TC while maintaining lower OAR doses. However, when anatomical changes were considered, improvement in TC from robust optimization was not observed in the worst-case perturbed plans. CONCLUSIONS For helical IMRT planning in localized prostate cancer, robust optimization provides benefits over PTV margin-based planning, including better OAR sparing, and increased robustness against systematic patient-setup errors.
A review on cable yarding operation performance and its assessment

ABSTRACT Cable yarding is a well-established practice for wood extraction in mountainous regions in most parts of the world where fully mechanized harvesting systems like harvester-forwarder combinations cannot operate due to steep terrain. Work- and time-studies are most relevant to create productivity models for cost estimation, simulation, system development or simply to compare different harvesting systems. The present work investigates 70 work studies on cable yarding operations, regarding assessment methods to establish a knowledge base on cable yarding performance models. A comprehensive literature research was performed to identify relevant studies. Information about the investigated extraction campaigns regarding site specifics, stock specifics, the utilized equipment alongside the work-study-methods, as well as the statistical approaches for model creation were collected. The data gained and the associated models were systematically analyzed to compare different yarding systems concerning their performance. A set of 98 models was selected for this purpose. Productivity of the investigated systems ranges from 1.5 m3/PSH to 69.3 m3/PSH with a mean value of 9.8 m3/PSH where PSH represents the productive system hour. A meta-analysis was performed with the data that is presented in the literature found to test several hypotheses concerning the influence of different parameters on the performance of yarding systems. Various relationships are so strong that regression analysis with the meta-dataset, that is mostly containing mean values presented, results in significant correlations. The diversity in problem formulation and the corresponding diversity in methodical approaches account for limitations in comparability of performance. The consultation of guiding literature on forest work studies can promote comparability between studies.
Explosives with Lined Cavities

Explosives detonated in contact with thick steel plates produce much deeper holes in the steel when there is a cavity in the explosive in contact with the plate. While this phenomenon has been known for more than 150 years, the enormous increase in penetrating power that can be produced by lining the explosive cavity with thin metal has been discovered only recently. During the war a number of light, low velocity, antitank weapons (e.g., the rocket‐propelled Bazooka) were developed which made use of this phenomenon to perforate thick armor plate.A fairly complete mathematical theory of this essentially new phenomenon is presented together with some of the experimental data that aided in the formulation and testing of the theory. The process is separated into two phases: first, the formation of part of the metal liner into a long thin jet traveling longitudinally at very high velocities (30,000 ft./sec.) and, second, the forcing aside of the target material by the extremely high pressures (0.3‐million atmo.
Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. However, need of larger training sets, computational complexity, and overfitting, etc. are the major issues with these techniques that still need to be addressed. In this work, a convolutional neural network (CNN) is developed that consists of smaller number of layers leading to lower computational burden. Some augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to generate additional samples increasing the training set without actually capturing more images. The CNN model is trained for apple crop using a publicly available dataset PlantVillage to identify Scab, Black rot, and Cedar rust diseases in apple leaves. The rigorous experimental results revealed that the proposed model is well fit to identify apple leaf diseases and achieves 98% classification accuracy. It is also evident from the results that it needs lesser amount of storage and takes smaller execution time than several existing deep CNN models. Although, there exist several CNN models for crop disease detection with comparable accuracy, but the proposed model needs lower storage and computational resources. Therefore, it is highly suitable for deploying in handheld devices.
The Fundamental Parameters of Cosmology

I describe the evidence for the big bang, including the various cosmological parameters. There is general concordance on their values, although the uncertainties remain large enough to justify the ensuing debate. Structure formation is reviewed, and here there is consensus on the hierarchical nature of the sequence of formation. The cosmic microwave background anisotropies provide a glimpse of the initial conditions that seeded structure, and the evolution of clouds of dark matter is an inevitable consequence. There are two outstanding mysteries: the very beginning, where a theory of quantum gravity is eagerly awaited, and the detailed nature of galaxy formation, where observations are well ahead of theory.
Robustness of Optimal Controlled Diffusions with Near-Brownian Noise via Rough Paths Theory

In this article we show a robustness theorem for controlled stochastic differential equations driven by approximations of Brownian motion. Often, Brownian motion is used as an idealized model of a diffusion where approximations such as Wong-Zakai, Karhnen-Lo\`eve or fractional Brownian motion are often seen as more physical. However, there has been extensive literature on solving control problems driven by Brownian motion and little on control problems driven by more realistic models that are only approximately Brownian. The question of robustness naturally arises from such approximations. We show robustness using rough paths theory, which allows for a pathwise theory of stochastic differential equations. To this end, in particular, we show that within the class of Lipschitz continuous control policies, an optimal solution for the Brownian idealized model is near optimal for a true system driven by a non-Brownian (but near-Brownian) noise.
BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning

Prediction, decision-making, and motion planning are essential for autonomous driving. In most contemporary works, they are considered as individual modules or combined into a multi-task learning paradigm with a shared backbone but separate task heads. However, we argue that they should be integrated into a comprehensive framework. Although several recent approaches follow this scheme, they suffer from complicated input representations and redundant framework designs. More importantly, they can not make long-term predictions about future driving scenarios. To address these issues, we rethink the necessity of each module in an autonomous driving task and incorporate only the required modules into a minimalist autonomous driving framework. We propose BEVGPT, a generative pre-trained large model that integrates driving scenario prediction, decision-making, and motion planning. The model takes the bird's-eye-view (BEV) images as the only input source and makes driving decisions based on surrounding traffic scenarios. To ensure driving trajectory feasibility and smoothness, we develop an optimization-based motion planning method. We instantiate BEVGPT on Lyft Level 5 Dataset and use Woven Planet L5Kit for realistic driving simulation. The effectiveness and robustness of the proposed framework are verified by the fact that it outperforms previous methods in 100% decision-making metrics and 66% motion planning metrics. Furthermore, the ability of our framework to accurately generate BEV images over the long term is demonstrated through the task of driving scenario prediction. To the best of our knowledge, this is the first generative pre-trained large model for autonomous driving prediction, decision-making, and motion planning with only BEV images as input.
Rethinking Relation Classification with Graph Meaning Representations

In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research. Despite the growing interest, a critical gap persists in understanding the exact influence of GMRs, particularly concerning relation extraction tasks. Addressing this, we introduce DAGNN-plus, a simple and parameter-efficient neural architecture designed to decouple contextual representation learning from structural information propagation. Coupled with various sequence encoders and GMRs, this architecture provides a foundation for systematic experimentation on two English and two Chinese datasets. Our empirical analysis utilizes four different graph formalisms and nine parsers. The results yield a nuanced understanding of GMRs, showing improvements in three out of the four datasets, particularly favoring English over Chinese due to highly accurate parsers. Interestingly, GMRs appear less effective in literary-domain datasets compared to general-domain datasets. These findings lay the groundwork for better-informed design of GMRs and parsers to improve relation classification, which is expected to tangibly impact the future trajectory of natural language understanding research.
Multi-Component Interventions in Older Adults Having Subjective Cognitive Decline (SCD)—A Review Article

Subjective cognitive decline (SCD) is one of those significant concerns faced by older individuals. Though it is predominantly self-reported, it is not an event that should be overlooked, considering its significant association with cognitive disorders like Alzheimer’s disease, mild cognitive impairment, and so on. This makes it imperative to find ways to manage the event to enhance the cognitive performance of older adults and/or suppress the rate at which cognitive decline results in impairment. While multiple interventions have been used for SCD, multi-component non-pharmacological interventions are beginning to gain more attention among researchers. This is due to how such interventions have effectively contributed to improved cognitive performance across different outcome domains. Against this backdrop, this literature review has been conducted to explore the different multi-component non-pharmacological interventions utilized in managing SCD. Papers from databases such as PubMed, Scopus, and EBSCO were retrieved, with relevant data being extracted on the subject matter to address the objective of this review.
Landau–Zener tunnelling in 2D periodic structures in the presence of a gauge field: II. Electric breakdown

We analyse dynamics of a quantum particle in a square lattice in the Hall configuration beyond the single-band approximation. For vanishing gauge (magnetic) field this dynamics is defined by the inter-band Landau–Zener tunnelling, which is responsible for the phenomenon known as the electric breakdown. We show that in the presence of a gauge field this phenomenon is absent, at least, in its common sense. Instead, the Landau–Zener tunnelling leads to the appearance of a finite current which flows in the direction orthogonal to the vector of a potential (electric) field.
Forced Remyelination Promotes Axon Regeneration in a Rat Model of Spinal Cord Injury

Spinal cord injuries result in the loss of motor and sensory functions controlled by neurons located at the site of the lesion and below. We hypothesized that experimentally enhanced remyelination supports axon preservation and/or growth in the total spinal cord transection in rats. Multifocal demyelination was induced by injection of ethidium bromide (EB), either at the time of transection or twice during transection and at 5 days post-injury. We demonstrated that the number of oligodendrocyte progenitor cells (OPCs) significantly increased 14 days after demyelination. Most OPCs differentiated into mature oligodendrocytes by 60–90 dpi in double-EB-injected rats; however, most axons were remyelinated by Schwann cells. A significant number of axons passed the injury epicenter and entered the distant segments of the spinal cord in the double-EB-injected rats. Moreover, some serotoninergic fibers, not detected in control animals, grew caudally through the injury site. Behavioral tests performed at 60–90 dpi revealed significant improvement in locomotor function recovery in double-EB-injected rats, which was impaired by the blockade of serotonin receptors, confirming the important role of restored serotonergic fibers in functional recovery. Our findings indicate that enhanced remyelination per se, without substantial inhibition of glial scar formation, is an important component of spinal cord injury regeneration.
Electromagnetic Scattering and Absorption by Arrays of Lossless/Lossy Metallic or Dielectric Strips

A numerical technique that requires neither extensive mathematical analysis nor much computational effort is introduced and used to calculate electromagnetic scattering from periodic structures. The method is capable of solving homogeneous or inhomogeneous, lossless or lossy, conductor or dielectric arrays of rectangular cylinders or strips. Numerical results are presented and compared against results for self-complementary gratings which are obtained by a rigorous Wiener-Hopf technique and serve as benchmarks. These comparisons show the efficacy and accuracy of our proposed technique. Further results are obtained and compared with those reported in the literature that are based on other numerical methods or approximations such as surface impedance models.
What is gratitude without a benefactor? A daily diary approach

ABSTRACT “Gratitude” has multiple meanings. One distinction is between gratitude with and without a benefactor. This paper presents two daily diary studies examining the experience of gratitude without a benefactor. In the first, some participants wrote each day about something good caused by someone else whereas others wrote about something good caused neither by self nor other. In the second, all participants wrote each day about something good caused neither by self nor other. Both studies included a novel measure of gratitude designed to distinguish gratitude with and without a benefactor. Across both studies, participants reported feeling gratitude even without a benefactor, and the novel measure of gratitude predicted daily gratitude even after controlling for other gratitude measures. In study 2, participants reported that benefactorless gratitude made them feel the motivational goals of being more obligated, spiritual, resourceful, indebted, generous, celebratory and wanting to help someone else, even after controlling for joy. Some analyses suggest that gratitude with a benefactor is particularly associated with obligation. For instance, in study 1, daily gratitude with a benefactor was more related to obligation than gratitude without a benefactor. These studies suggest that distinctions between gratitude with versus without a benefactor warrant continued exploration.
Measures of Nursing Environment Multidimensionality and Patient Centricity Using Importance-Performance Map Analysis

Background: The nursing environment has become a consideration for an organization in improving service quality, especially in the implementation of patient-centred care. The various dimensions make it necessary to know which sectors need to be prioritized. However, there is still limited research that is more specific in linking the dimensions to become more operational. Purpose: This study aimed to analyze the relationship between nursing environment dimensions and patient centricity through the Importance-Performance Map Analysis (IPMA). Methods: A quantitative survey with a cross-sectional approach was conducted in June 2022 to test the conceptual framework on the population obtained from non-managerial inpatient nurses who worked for above two years in a general hospital in East Java, Indonesia. The constructs were measured using a set of indicators in The Practice Environment Scale of the Nursing Work Index (PES-NWI) and Patient Centricity. During the data collection, a total sampling technique was performed, resulting in 89 respondents being acquired. The data were analyzed through partial least squared structural equation modelling (PLS-SEM). Results: At the construct level, the mean total effect and performance were 0.192 and 56.302, respectively. From the IPMA chart, it was found that the construct in the right lower quadrant with the largest total effect value but not having adequate performance was nursing manager ability (total effect: 0.294, performance: 34.563), making it a construct with the highest importance and requiring priority for improvement among all dimensions.Conclusion: The nursing environment dimensions have to get attention to achieve patient centricity in inpatient ward nurses, where nursing manager ability is the dimension that has the most vulnerable performance. Leadership development is needed for the head nurse of the inpatient unit to strengthen the ability to lead and change the culture in nurturing subordinates so that kinship between nursing professionals can be established.
NUMERICAL-SIMULATION OF EXCAVATION IN ELASTOPLASTIC SOILS

A finite element method for simulating excavation in elastoplastic soils which produces a "unique" solution for any number of excavation stages is presented. The proposed method satisfies the uniqueness principle for elastic materials postulated by Ishihara, and degenerates to the methods presented in the Adina code and by Brown and Booker and Ghaboussi and Pecknold for the simple elastic cases. The non-linear (elastoplastic) finite element equations are derived from a variational formulation which accounts for time-varying problem domain and boundaries. The roots of these non-linear equations are solved by Newton's method, using the notion of the consistent tangent operator proposed by Simo and Taylor in conjunction with a numerical algorithm based on the "radial-return" concept for integrating stresses. Numerical examples demonstrate that in the elastic case the solution to problems involving a "shrinking" (excavation) or "expanding" (fill) domain is superposable and does not depend on the number of construction stages. For a monotonically shrinking elastoplastic domain and the von Mises and Drucker-Prager yield models, it is shown that the proposed method also produces a "unique" solution independent of the number of excavation stages. Finally, the asymptotic quadratic convergence exhibited by the proposed method is demonstrated to show the efficiency engendered by the technique described. (Author/TRRL)
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

Graph neural networks (GNNs) have demonstrated significant promise in modelling relational data and have been widely applied in various fields of interest. The key mechanism behind GNNs is the so-called message passing where information is being iteratively aggregated to central nodes from their neighbourhood. Such a scheme has been found to be intrinsically linked to a physical process known as heat diffusion, where the propagation of GNNs naturally corresponds to the evolution of heat density. Analogizing the process of message passing to the heat dynamics allows to fundamentally understand the power and pitfalls of GNNs and consequently informs better model design. Recently, there emerges a plethora of works that proposes GNNs inspired from the continuous dynamics formulation, in an attempt to mitigate the known limitations of GNNs, such as oversmoothing and oversquashing. In this survey, we provide the first systematic and comprehensive review of studies that leverage the continuous perspective of GNNs. To this end, we introduce foundational ingredients for adapting continuous dynamics to GNNs, along with a general framework for the design of graph neural dynamics. We then review and categorize existing works based on their driven mechanisms and underlying dynamics. We also summarize how the limitations of classic GNNs can be addressed under the continuous framework. We conclude by identifying multiple open research directions.
Hydrogel viscoelasticity modulates migration and fusion of mesenchymal stem cell spheroids

Abstract Multicellular spheroids made of stem cells can act as building blocks that fuse to capture complex aspects of native in vivo environments, but the effect of hydrogel viscoelasticity on cell migration from spheroids and their fusion remains largely unknown. Here, we investigated the effect of viscoelasticity on migration and fusion behavior of mesenchymal stem cell (MSC) spheroids using hydrogels with a similar elasticity but different stress relaxation profiles. Fast relaxing (FR) matrices were found to be significantly more permissive to cell migration and consequent fusion of MSC spheroids. Mechanistically, inhibition of ROCK and Rac1 pathways prevented cell migration. Moreover, the combination of biophysical and biochemical cues provided by fast relaxing hydrogels and platelet‐derived growth factor (PDGF) supplementation, respectively, resulted in a synergistic enhancement of migration and fusion. Overall, these findings emphasize the important role of matrix viscoelasticity in tissue engineering and regenerative medicine strategies based on spheroids.