Numerical simulation of flow and heat transfer of continous cast steel slab under traveling magnetic field

A unified numerical model for simulating solidification transport phenomena (STP) of steel slab in electromagnetic continuous casting (EMCC) process was developed. In order to solve the multi-physics fields coupled problem conveniently, the complicated bidirectional coupled process between EM and STP was simplified as a unidirectional one, and a FEM/FVM-combined numerical simulation technique was adopted. The traveling magnetic fields (TMFs) applied to the EMCC process were calculated using the ANSYS11.0 software, and then the EM-data output by ANSYS were converted to FVM-format using a data-format conversion program developed previously. Thereafter, the governing equations were solved using a pressure-based Direct-SIMPLE algorithm. The simulation results of the STP in CC-process show that, due to the influences of Lorentz force and Joule heat, the two strong circulating flows and the temperature field can be obviously damped and changed once TMF with one pair of poles (1-POPs) or 2-POPs is applied, which would accordingly improve the quality of casting. It was found in the present research that the integrated actions of 2-POPs TMF are superior to 1-POPs. All the computations indicate that the present numerical model of EM-STP as well as the FEM/FVM-combined technique is successful

A Microarray Study of Middle Cerebral Occlusion Rat Brain with Acupuncture Intervention

Microarray analysis was used to investigate the changes of gene expression of ischemic stroke and acupuncture intervention in middle cerebral artery occlusion (MCAo) rat brain. Results showed that acupuncture intervention had a remarkable improvement in neural deficit score, cerebral blood flow, and cerebral infarction volume of MCAo rats. Microarray analysis showed that a total of 627 different expression genes were regulated in ischemic stroke. 417 genes were upregulated and 210 genes were downregulated. A total of 361 different expression genes were regulated after acupuncture intervention. Three genes were upregulated and 358 genes were downregulated. The expression of novel genes after acupuncture intervention, including Tph1 and Olr883, was further analyzed by Real-Time Quantitative Polymerase Chain Reaction (RT-PCR). Upregulation of Tph1 and downregulation of Olr883 indicated that the therapeutic effect of acupuncture for ischemic stroke may be closely related to the suppression of poststroke depression and regulation of olfactory transduction. In conclusion, the present study may enrich our understanding of the multiple pathological process of ischemic brain injury and indicate possible mechanisms of acupuncture on ischemic stroke

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).ConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG

The spanning kk-trees, perfect matchings and spectral radius of graphs

A $k$-tree is a spanning tree with every vertex of degree at most $k$. In this paper, we provide some sufficient conditions, in terms of the (adjacency and signless Laplacian) spectral radius, for the existence of a $k$-tree in a connected graph of order $n$, and a perfect matching in a balanced bipartite graph of order $2n$ with minimum degree $\delta$, respectively.Comment: 12 page

More Features in Bound Representations Does Not Require Extra Object-based Attention in Working Memory

PURPOSE: Feature binding is a core concept in many research fields, including the study of working memory (WM).We recently proposed that binding in WM is not passive, but requires more object-based attention to actively bind distinct single features into a coherent unit (Gao et al., Attention, Perception, &amp; Psychophysics, 2017; Shen, Huang, &amp; Gao, Journal of Experimental Psychology: Human Perception and Performance, 2015). However, a hallmark of object-based attention&mdash;the amount of attention is not modulated by the number features contained in an object&mdash;has not been examined. In the current study, we closed this gap by examining whether this hallmark of object-based attention still holds in WM. METHODS: In two experiments, we required the participants to memorize three bound representations, and manipulated the number of features (2 vs. 3 features) contained in each binding. To examine the role of object-based attention in retaining bindings in WM, we also manipulated whether a secondary task consuming object-based attention was interpolated into the maintenance phase of WM (with vs. without secondary task). If more object-based attention was required after an extra feature was added into the bound representation, then the secondary task would result in worse performance for 3-featured binding than 2-featured binding. RESULTS: In two experiments, we consistently found that the added secondary task significantly impaired the binding performance. However, the added secondary task impaired the 2-featured and 3-featured bindings to the same extent. CONCLUSION: The number of features contained in binding does not modulate the required object-based attention for binding in WM, suggesting that WM and perception share the same hallmark of object-based attention.</p

Retaining Bindings of Integral Features in Working Memory: The Role of Object-based Attention

PURPOSE: Over the past decade, it has been debated whether retaining bindings in working memory (WM) requires more attention than retaining constituent features, focusing on domain-general attention and space-based attention. Recently we proposed that retaining bindings in WM needs more object-based attention than retaining constituent features (Gao et al., 2017; Shen, Huang, &amp; Gao, 2015). However, the composed features in the tested bindings all belong to separable feature dimensions. It has been suggested that there are two types of feature relations: Separable features (e.g., color and shape in a colored shape) and integral features (e.g., width and height of a rectangle). While our brain encodes separable features independently, it is difficult to encode the integral features separately. Consequently, the object-based attention hypothesis of retaining bindings in WM may be constrained to separable features, and retaining bindings of integral features does not require more object-based attention than the constitute single features. METHODS: In the current study we addressed this issue by requiring the participants to memorize both width and height of three rectangles or the binding between the two feature dimensions. In the critical condition, we added a secondary transparent motion task during the delay interval of the change-detection task, such that the secondary task competed for object-based attention with the to-be-memorized stimuli. If more object-based attention is required for retaining bindings than for retaining constituent features, the secondary task should impair the binding performance to a larger degree relative to the performance of constituent features. RESULTS: In contrast to the prediction of object-based attention hypothesis, the added secondary task equally impaired the performance of single features and binding. CONCLUSION: Retaining bindings of integral features in WM does not require more object-based attention than the constitute single features, providing a key constraint to the object-based attention hypothesis.</p

An improved forgetting factor recursive least square and unscented particle filtering algorithm for accurate lithium-ion battery state of charge estimation.

As an indispensable part of the battery management system, accurately predicting the estimation of the state of charge (SOC) has attracted more attention, which can improve the efficiency of battery use and ensure its safety performance. Taking the ternary lithium battery as the research object, we present an improved forgetting factor recursive least square (IFFRLS) method for parameter identification and a joint unscented particle filter algorithm for SOC estimation. First, take advantage of the particle swarm optimization (PSO) algorithm to select the optimal parameter initial value and forgetting factor value to improve the precision of the FFRLS method. At the same time, make use of the unscented Kalman algorithm (UKF) as the density function of the particle filter algorithm (PF) to form the unscented particle filtering (UPF) algorithm. Then, the IFFRLS method and UPF algorithm are proposed in this paper. The different working conditions results show that the proposed algorithm estimates the SOC with good convergence and high system robustness. The final estimation error of the algorithm is stable at 1.6 %, which is lower than the errors of the currently used EKF algorithm, UKF algorithm and PF algorithm, which provides a reference for future research on lithium-ion batteries