The void in solder layer is one of the most important aging form of Insulated Gate Bipolar Transistor (IGBT) module. In this paper, the influence mechanism of different void fractions on the thermal conductivity and specific heat capacity of the solder layer is studied. An improved Cauer model considering the void damage of the solder layer is established, and its model parameter extraction method is proposed. In addition, the temperature dependence of Cauer model and its parameter is discussed in this paper. In order to facilitate the experimental study of void damage in solder layer, we customized IGBT modules with different void fractions. The influence of void morphology on the thermal properties of solder layer is studied by finite element method, and the research purpose is to verify that void fraction can represent the effect of the voids on IGBT thermal characteristics. Considering the thermal resistance and thermal capacitance affected by the voids in solder layer, their calculation methods are given in detail. Then, under different loads, the IGBT modules with different void fractions are tested. The robustness of the improved Cauer model is verified by the experimental and numerical simulation results. The results show that the improved Cauer model has higher accuracy than the traditional Cauer model.

Silicon carbide metal-oxide-semiconductor field-effect transistor (SiC MOSFET) has become a promising device due to its excellent material properties. Junction temperature is an important parameter and a significant health index. In this article, drain current during turn-on transient is taken as the thermo-sensitive electrical parameter (TSEP) to monitor the junction temperature of SiC MOSFET. Based on the physical properties of wide bandgap semiconductor materials, the switching behaviors of SiC MOSFET and the variation of drain current with temperature were studied. The influence of temperature dependence of threshold voltage and carrier mobility on drain current is analyzed. It is proven that the drain current has a positive temperature coefficient during turn-on transient. Finally, the validity of the proposed method is verified by the theoretical analysis and experiments.

Non-intrusive load monitoring (NILM) is an important research direction and development goal on the distribution side of smart grid, which can significantly improve the timeliness of demand side response and users’ awareness of load. Due to rapid development, deep learning becomes an effective way to optimize NILM. In this paper, we propose a novel load identification method based on long short term memory (LSTM) on deep learning. Sequence-to-point (seq2point) learning is introduced into LSTM. The innovative combination of the LSTM and the seq2point brings their respective advantages together, so that the proposed model can accurately identify the load in process of time series data. In this paper, we proved the feature of reducing identification error in the experimental data, from three datasets, UK-DALE dataset, REDD dataset, and REFIT dataset. In terms of mean absolute error (MAE), the three datasets have increased by 15%, 14%, and 18% respectively; in terms of normalized signal aggregate error (SAE), the three datasets have increased by 21%, 24%, and 30% respectively. Compared with the existing models, the proposed model has better accuracy and generalization in identifying three open source datasets.

Astrocytes have a regulatory function on the central nervous system (CNS), especially in the temperature-sensitive hippocampal region. In order to explore the thermosensitive dynamic mechanism of astrocytes in the CNS, we establish a neuron–astrocyte minimum system to analyze the synchronization change characteristics based on the Hodgkin–Huxley model, in which a pyramidal cell and an interneuron are connected by an astrocyte. The temperature range is set as 0 °C–40 °C to juggle between theoretical calculation and the reality of a brain environment. It is shown that the synchronization of thermosensitive neurons exhibits nonlinear behavior with changes in astrocyte parameters. At a temperature range of 0 °C–18 °C, the effects of the astrocyte can provide a tremendous influence on neurons in synchronization. We find the existence of a value for inositol triphosphate (IP₃) production rate and feedback intensities of astrocytes to neurons, which can ensure the weak synchronization of two neurons. In addition, it is revealed that the regulation of astrocytes to pyramidal cells is more sensitive than that to interneurons. Finally, it is shown that the synchronization and phase transition of neurons depend on the change in Ca²⁺ concentration at the temperature of weak synchronization. The results in this paper provide some enlightenment on the mechanism of cognitive dysfunction and neurological disorders with astrocytes.

Background: Parkinson’s disease (PD) is a common neurodegenerative disorder in which over 80% of patients experience severe sleep disorders. Understanding the neural dynamical mechanisms underlying these sleep disturbances is essential for developing targeted therapeutic strategies. Methods: We construct a basal ganglia–thalamocortical–pedunculopontine nucleus (BGTC–PPN) network model by combining Hodgkin–Huxley and Izhikevich neuron models to simulate both healthy and PD network dynamics. We systematically compare the discharge patterns, synchronization indices and power spectral densities of key nuclei between healthy and PD conditions. In addition, we use coherence analysis to characterize the frequency-dependent coupling between the PPN and basal ganglia nuclei, and investigate deep brain stimulation (DBS) modulation effects on the PD network using rectangular-wave stimulation at three target nuclei. Results: Under the PD condition, neuronal activity in the GPi and STN is increased but synchronization in the GPe and PPN is enhanced. β band oscillations are increased, and low-frequency activity in the PPN is disrupted. Coherence analysis reveals frequency-dependent coupling between the PPN and basal ganglia during PD-related network states. Comparison of different DBS targets demonstrates distinct effects. Within our 5-Hz-resolution frequency sweep, STN-DBS broadly suppresses β oscillations and, at approximately 70 Hz, minimizes the deviation of both the δ and β bands from the healthy reference; PPN-DBS enhances δ band activity at low stimulation frequencies or suppresses β synchronization at high frequencies, but these two effects do not co-occur within a single frequency window; GPi-DBS yields a larger increase in δ band power relative to the PD baseline near 55 Hz and suppresses β power in the GPi and PPN, yet the STN β deviation remains elevated across the scanned frequencies. Conclusions: PD-related pathological changes extend beyond motor circuitry and alter BGTC–PPN network dynamics that may be relevant to sleep. The target-dependent and frequency-dependent effects of DBS indicate that STN-DBS at approximately 70 Hz concurrently minimizes the deviation of both the δ and β bands from the healthy reference, whereas GPi-DBS and PPN-DBS modulate the two bands in a frequency-decoupled manner within our 5 Hz resolution frequency sweep. These findings provide a computational framework for optimizing DBS intervention strategies for PD-related sleep dysfunction.