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Huave exhibits "mobile affixation", whereby the placement of individual affixes varies depending on the phonological properties of affix and stem. Kim (2008, 2010) analyzed these facts within a cyclic cophonology approach through the interaction between alignment constraints (McCarthy & Prince, 1993) and the phonological constraints *CC and DEP. Using the same core components, this paper develops an alternative, fully parallel analysis. In this approach, morphemes are unordered in the phonological input, every morpheme is indexed to its own (crucially gradient) alignment constraint, and output ordering is determined entirely through simultaneous constraint interaction. Notably, this analysis captures a previously unnoticed generalization about the stability of left/right order between affixes in the language. The main contribution of this new analysis is that it provides a principled distinction between mobile and immobile affixes: the order of affixes, as determined by the relative ranking of their alignment constraints, correlates with their ability to trigger epenthesis, as determined by their ranking relative to DEP. This follows from a parallel approach with a single language-wide ranking, but not from a cyclic cophonology account. Furthermore, this distinction can be tied directly to morphosyntactic structure via Zukoff's (2020) "Mirror Alignment Principle" and considerations of Base-Derivative faithfulness (Benua, 1997).

The ultimate aim of both Eckhart’s philosophy and Vedānta philosophy is to attain oneness with God/Brahman. Nevertheless, their different philosophical starting points and the conflict between the sublime ideal of the theory and reality means that their philosophies present a structural symmetry. They both have to face two dilemmas: “How can we claim that humans are already one with God?” and “Why is it that humans are not already one with God?”. Eckhart’s inherited tradition emphasizes the distinction between humans and God, while the Vedānta philosophical tradition emphasizes that “I am Brahman”. Each of them starts from one pole of the seesaw of the dilemma and encounters the other’s issue at the other pole. Eventually, they converge at the point of balance, with unity with God/Brahman realized in all human activities. Here, this worldly life becomes significant, all human work expresses the Divinity, and the importance of God is replaced by an impersonal Divinity that combines being and nothingness.

This study proposed a combined solar-assisted heat pump (SAHP) system that could operate in the serial mode or parallel mode. For this proposed system, a stable year-round operation could be achieved without the assistance of electric heating or low-temperature heat pump. By analyzing the heat balance equations, a correlation of the combined SAHP system for the two modes switched was obtained, which provided a theoretical basis for the optimal operation of this system. In addition, the performance of the proposed system applied in a university bathroom in Xi’an district was investigated using TRNSYS. The results illustrated that compared to the serial and parallel systems, the proposed system exhibited a good performance on energy efficiency. The annual average coefficient of performance (COP) of the proposed system was 5.7, obviously higher than those of the serial system and the parallel system, which were 3.3 and 4.3, respectively. Therefore, the results in this study could provide the theoretical guidance and reference for practical engineering design.

Abstract In cognitive radio (CR) systems, the accurate detection of spectrum holes is a cornerstone for efficient spectrum utilization. However, the increasing complexity of CR environments, particularly those with multiple primary users (PUs), has made precise spectrum sensing a paramount challenge. To address this challenge, this study introduces the ATC model, a novel deep learning architecture that integrates a parallel combination of attention mechanism-based networks and a Convolutional Neural Network (CNN). This hybrid design enables the model to capture both spatial and temporal features from the distinct statistics of sensing signals, thereby enhancing the accuracy of spectrum state detection. The model employs a Graph Attention Network (GAT) to extract complex topological features from graph-structured data derived from received signal strength, dynamically highlighting the most relevant information. To complement this, a CNN processes the sample covariance matrix of sensing signals, unlocking localized statistical correlations and hierarchical feature representations by treating the matrix as an image. Temporal dynamics, such as PU activity patterns, are modeled using a Transformer encoder, which leverages a self-attention mechanism to learn sequential features effectively. The proposed model is evaluated using both simulated and real-world datasets. For the simulated datasets, the model is assessed and compared with baseline methods under multi-PU scenarios across different channel models. For the real-world dataset, the experimental setup is configured for a single-PU scenario due to practical data collection limitations. In both cases, the ATC model demonstrates improved performance over the benchmarked spectrum sensing methods, exhibiting higher accuracy and robustness within the respective evaluation settings.