Radiative heat transfer analysis of a concave porous fin under the local thermal non-equilibrium condition:application of the clique polynomial method and physics-informed neural networks

The heat transfer through a concave permeable fin is analyzed by the local thermal non-equilibrium(LTNE)model.The governing dimensional temperature equations for the solid and fluid phases of the porous extended surface are modeled,and then are nondimensionalized by suitable dimensionless terms.Further,the obtained nondimensional equations are solved by the clique polynomial method(CPM).The effects of several dimensionless parameters on the fin's thermal profiles are shown by graphical illustrations.Additionally,the current study implements deep neural structures to solve physics-governed coupled equations,and the best-suited hyperparameters are attained by comparison with various network combinations.The results of the CPM and physicsinformed neural network(PINN)exhibit good agreement,signifying that both methods effectively solve the thermal modeling problem.

An Open Source Toolkit for Identifying Comparative Space-time Research Questions

Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education.

A Model Effective Mass Quantum Anharmonic Oscillator and Its Thermodynamic Characterization

Using a model anharmonic oscillator with asymptotically decreasing effective mass to study the effect of compositional grading on the quantum mechanical properties of a semiconductor heterostructure, we determine the exact bound states and spectral values of the system. Furthermore, we show that ordering ambiguity only brings about a spectral shift on the quantum anharmonic oscillator with spatially varying effective mass. A study of thermodynamic properties of the system reveals a resonance condition dependent on the magnitude of the anharmonicity parameter. This resonance condition is seen to set a critical value on the said parameter beyond which a complex valued entropy which is discussed, emerges.

Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

Background TDP-43 proteinopathies represent a spectrum of neurological disorders,anchored clinically on either end by amyotrophic lateral sclerosis(ALS)and frontotemporal degeneration(FTD).The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes,highlighting its heterogeneity.This study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum.Methods We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD(bvFTD;with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants),103 ALS,and 47 ALS-FTD patients with likely TDP-43.A Subtype and Stage Inference(SuStaIn)model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns,and we related subtypes and stages to clinical,genetic,and neuropathological features of disease.Results SuStaIn identified three novel subtypes:two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions,and a normal-appearing group without obvious brain atrophy.The limbic-predominant subtype tended to present with more impaired cognition,higher frequencies of pathogenic variants in TBK1 and TARDBP genes,and a higher proportion of TDP-43 types B,E and C.In contrast,the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A.The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients,higher cognitive capacity,higher proportion of lower motor neuron onset,milder motor symptoms,and lower frequencies of genetic pathogenic variants.The overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration,higher King’s stage,and cognitive decline.Additionally,SuStaIn stages differed across clinical phenotypes,genotypes and types of TDP-43 pathology.Conclusions Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo,each with distinct brain atrophy,clinical,genetic and pathological patterns.