Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling. Yet, despite their promise, the transposition of these techniques to the neuroimaging domain has been challenging due to the expansive number of potential preprocessing pipelines and the large parameter search space for graph-based dataset construction. In this paper, we introduce NeuroGraph, a collection of graph-based neuroimaging datasets, and demonstrated its utility for predicting multiple categories of behavioral and cognitive traits. We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking. Additionally, we provide generic frameworks for learning on both static and dynamic graphs. Our extensive experiments lead to several key observations. Notably, using correlation vectors as node features, incorporating larger number of regions of interest, and employing sparser graphs lead to improved performance. To foster further advancements in graph-based data driven neuroimaging analysis, we offer a comprehensive open-source Python package that includes the benchmark datasets, baseline implementations, model training, and standard evaluation.
This study is aimed at determining the functional effect of snakehead fish gelatin as a binder on the characteristics and shelf life of beef cheek-based emulsion sausage compared with bovine commercial gelatin. The level of snakehead fish gelatin used was 0%, 1%, 2%, and 3%, while that of bovine commercial gelatin was 2% with a storage time of 0 to 28 days in the refrigerator (4 ± 2°C). Emulsion stability, cooking loss, proximate composition, texture profile, and microstructure of sausage were initially determined before storage; then, observations were made every seven days to determine the shelf life of sausages based on pH, antioxidant activity, and TBA reactivity. Characteristics such as emulsion stability, proximate composition, and texture profile were influenced by the treatment (p < 0.05). The gelatin level significantly affected the water holding capacity of sausages (p < 0.05), but the storage time did not (p > 0.05). On the other hand, the pH, TBA reactivity, and antioxidant activity of sausages were not only affected by gelatin level (p < 0.05) but also by storage time (p < 0.05). The sausage microstructure confirms the use of 2% snakehead fish gelatin to make sausages with properties similar to 2% commercial bovine gelatin. The byproduct of the snakehead fish industry can be used as a gelatin alternative to produce sausages. This gelatin has the potential as a binder, which can functionally improve sausage characteristics. This effectiveness can boost the water holding capacity of sausages, although it has not been effective in inhibiting fat oxidation which causes an increase in malonaldehyde levels.
The main purpose of the current article is to scrutinize the flow of hybrid nanoliquid (ferrous oxide water and carbon nanotubes) (CNTs + Fe3O4/H2O) in two parallel plates under variable magnetic fields with wall suction/injection. The flow is assumed to be laminar and steady. Under a changeable magnetic field, the flow of a hybrid nanofluid containing nanoparticles Fe3O4 and carbon nanotubes are investigated for mass and heat transmission enhancements. The governing equations of the proposed hybrid nanoliquid model are formulated through highly nonlinear partial differential equations (PDEs) including momentum equation, energy equation, and the magnetic field equation. The proposed model was further reduced to nonlinear ordinary differential equations (ODEs) through similarity transformation. A rigorous numerical scheme in MATLAB known as the parametric continuation method (PCM) has been used for the solution of the reduced form of the proposed method. The numerical outcomes obtained from the solution of the model such as velocity profile, temperature profile, and variable magnetic field are displayed quantitatively by various graphs and tables. In addition, the impact of various emerging parameters of the hybrid nanofluid flow is analyzed regarding flow properties such as variable magnetic field, velocity profile, temperature profile, and nanomaterials volume fraction. The influence of skin friction and Nusselt number are also observed for the flow properties. These types of hybrid nanofluids (CNTs + Fe3O4/H2O) are frequently used in various medical applications. For the validity of the numerical scheme, the proposed model has been solved by another numerical scheme (BVP4C) in MATLAB.
The introduction of hybrid nanofluids is an important concept in various engineering and industrial applications. It is used prominently in various engineering applications, such as wider absorption range, low-pressure drop, generator cooling, nuclear system cooling, good thermal conductivity, heat exchangers, etc. In this article, the impact of variable magnetic field on the flow field of hybrid nano-fluid for the improvement of heat and mass transmission is investigated. The main objective of this study is to see the impact of hybrid nano-fluid (ferrous oxide water and carbon nanotubes) CNTs-Fe3O4, H2O between two parallel plates with variable magnetic field. The governing momentum equation, energy equation, and the magnetic field equation have been reduced into a system of highly nonlinear ODEs by using similarity transformations. The parametric continuation method (PCM) has been utilized for the solution of the derived system of equations. For the validity of the model by PCM, the proposed model has also been solved via the shooting method. The numerical outcomes of the important flow properties such as velocity profile, temperature profile and variable magnetic field for the hybrid nanofluid are displayed quantitatively through various graphs and tables. It has been noticed that the increase in the volume friction of the nano-material significantly fluctuates the velocity profile near the channel wall due to an increase in the fluid density. In addition, single-wall nanotubes have a greater effect on temperature than multi-wall carbon nanotubes. Statistical analysis shows that the thermal flow rate of (Fe3O4-SWCNTs-water) and (Fe3O4-MWCNTs-water) rises from 1.6336 percent to 6.9519 percent, and 1.7614 percent to 7.4413 percent, respectively when the volume fraction of nanomaterial increases from 0.01 to 0.04. Furthermore, the body force accelerates near the wall of boundary layer because Lorentz force is small near the squeezing plate, as the current being almost parallel to the magnetic field.
Ethereum, the second-largest cryptocurrency after Bitcoin, has attracted wide attention in the last few years and accumulated significant transaction records. However, the underlying Ethereum network structure is still relatively unexplored. Also, very few attempts have been made to perform link predictability on the Ethereum transactions network. This paper presents a Detailed Analysis of the Ethereum Network on Transaction Behavior, Community Structure, and Link Prediction (DANET) framework to investigate various valuable aspects of the Ethereum network. Specifically, we explore the change in wealth distribution and accumulation on Ethereum Featured Transactional Network (EFTN) and further study its community structure. We further hunt for a suitable link predictability model on EFTN by employing state-of-the-art Variational Graph Auto-Encoders. The link prediction experimental results demonstrate the superiority of outstanding prediction accuracy on Ethereum networks. Moreover, the statistic usages of the Ethereum network are visualized and summarized through the experiments allowing us to formulate conjectures on the current use of this technology and future development.
Repetitive transcranial magnetic stimulation (rTMS) induces lateralized speech arrest consistent with cerebral dominance for language. Studies of language cerebral dominance in differently handed healthy subjects have been limited. Using a focal magnetic coil, we examined the degree of consistency between handedness as evaluated by the Stanley Coren Score and hemispheric dominance for language as determined by rTMS in 25 right- and 25 left-handed medical students. They were categorized according to the score into 24 strongly right-handed, 1 moderately right-handed, 19 strongly left-handed, 3 moderately left-handed and 3 ambidextrous (equally-handed). In the strongly right-handed subjects, left-sided language cerebral dominance was recorded in 87.5% of the subjects, and bilateral cerebral representation in 8.2%, and right-sided language cerebral dominance in 4.2%. In the strongly left-handed subjects, 73.7% had left-side language cerebral dominance, 15.8% had bilateral cerebral representation and 10.5% had right-side cerebral language dominance. In mixed handed subjects (moderately right, left and ambidextrous), bilateral cerebral representation was observed in 57% and left-side cerebral language dominance in 43%. There were 27 subjects who developed speech arrest at 140% of motor threshold, the others developed speech arrest at lower intensities. Speech lateralized to the left-side cerebral dominance in strongly right- and left-handed subjects, but bilateral cerebral representation was frequent in mixed handedness and right-sided cerebral dominance rarely occurred.
Dominance, Cerebral/physiology , Functional Laterality/physiology , Speech/physiology , Adult , Brain/physiology , Electric Stimulation , Humans , Magnetics , Male
