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Heat exchange between a solid material and the gas environment is critical for the heat dissipation of miniature electronic devices. In this aspect, existing experimental studies focus on non-porous structures such as solid thin films, nanotubes, and wires. In this work, the proposed two-layer model for the heat transfer coefficient (HTC) between a solid sample and the surrounding air is extended to 70-nm-thick nanoporous Si thin films that are patterned with periodic rectangular nanopores having feature sizes of 100–400 nm. The HTC values are extracted using the 3[Formula: see text] method based on AC self-heating of a suspended sample with better accuracy than steady-state measurements in some studies. The dominance of air conduction in the measured HTCs is confirmed by comparing measurements with varied sample orientations. The two-layer model, developed for nanotubes, is still found to be accurate when the nanoporous film is simply treated as a solid film in the HTC evaluation along with the radiative mean beam length as the characteristic length of the nanoporous film. This finding indicates the potential of increasing HTC by introducing ultra-fine nanoporous patterns, as guided by the two-layer model. 

Abstract. Local spatiotemporal nonstationarity occurs in various naturaland socioeconomic processes. Many studies have attempted to introduce timeas a new dimension into a geographically weighted regression (GWR) model,but the actual results are sometimes not satisfying or even worse than theoriginal GWR model. The core issue here is a mechanism for weighting the effectsof both temporal variation and spatial variation. In many geographical andtemporal weighted regression (GTWR) models, the concept of time distance hasbeen inappropriately treated as a time interval. Consequently, the combinedeffect of temporal and spatial variation is often inaccurate in theresulting spatiotemporal kernel function. This limitation restricts theconfiguration and performance of spatiotemporal weights in many existingGTWR models. To address this issue, we propose a new spatiotemporal weightedregression (STWR) model and the calibration method for it. A highlight ofSTWR is a new temporal kernel function, wherein the method for temporalweighting is based on the degree of impact from each observed point to aregression point. The degree of impact, in turn, is based on the rate ofvalue variation of the nearby observed point during the time interval. Theupdated spatiotemporal kernel function is based on a weighted combination ofthe temporal kernel with a commonly used spatial kernel (Gaussian orbi-square) by specifying a linear function of spatial bandwidth versus time.Three simulated datasets of spatiotemporal processes were used to test theperformance of GWR, GTWR, and STWR. Results show that STWR significantlyimproves the quality of fit and accuracy. Similar results were obtained byusing real-world data for precipitation hydrogen isotopes (δ2H) in the northeastern United States. The leave-one-out cross-validation(LOOCV) test demonstrates that, compared with GWR, the total predictionerror of STWR is reduced by using recent observed points. Predictionsurfaces of models in this case study show that STWR is more localized thanGWR. Our research validates the ability of STWR to take full advantage ofall the value variation of past observed points. We hope STWR can bringfresh ideas and new capabilities for analyzing and interpreting localspatiotemporal nonstationarity in many disciplines.