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A compact-size fiber optic accelerometer was designed to achieve both high resolution and wide dynamic range concurrently. An optical cavity, with its length modulated by a piezoelectric oscillator at audio frequency, is utilized to resolve the nanometer-level displacement of a silicon micro-mirror which serves as an inertial mass for acceleration sensing. Strain analysis of the proof-mass flexure structure by Cosmosworks was carried out to calculate responsivity and resonance frequency for a comparison with experimental measurement. The responsivity below the structural resonance frequency of 160 Hz reaches 36 dB re 1 rad/g. Performance measurement demonstrated that the accelerometer was able to resolve an acceleration of 48 ng Hz−1/2 with a dynamic range of 2 × 107.

In this paper, we consider the number of spanning trees in circulant graphs. For any class of odd valent circulant graphs C2n(a1; a2; : : : ; a k−1 ; n), where a1; a2; : : : ; a k−1 are ÿxed jumps and n varies, some formulas, asymptotic behaviors and linear recurrence relations for the number of its spanning trees are obtained, and some known results on the ones in even valent circulant graphs Cn(a1; a2; : : : ; a k ) are improved.

The vegetation landscape in urban green space has been shown to provide great psychological benefits to people. Flower border is a well-designed small-scale vegetation landscape with the advantages of color and vegetation richness. This study focused on the effects of the visual attributes of flower borders on the aesthetic preference and emotional perception. The face recognition measurement method was used to obtain the emotional perception and the questionnaire survey method was used to measure the aesthetic preference. The results indicated the following: (1) regarding the ‘color features’ factor, high proportions of cool color and green vegetation significantly increased aesthetic preference and emotional valence, while the proportion of warm color had a negative effect on valence; (2) the ‘visual attractiveness’ (color brightness, and visual richness) and ‘color configuration’ (number of plant patches and number of color hues) factor was positively associated with aesthetic preference and emotional valence; (3) aesthetic preference was significantly related to emotional valence; (4) males expressed higher aesthetic preference and valence for flower border images than females. The results are expected to improve the aesthetic quality of flower borders and to promote public emotional health through the effective design of urban vegetation landscapes.

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Emotional responses are significant for understanding public perceptions of urban green space (UGS) and can be used to inform proposals for optimal urban design strategies to enhance public emotional health in the times of COVID-19. However, most empirical studies fail to consider emotion-oriented landscape assessments under dynamic perspectives despite the fact that individually observed sceneries alter with angle. To close this gap, a real-time sentimental-based landscape assessment framework is developed, integrating facial expression recognition with semantic segmentation of changing landscapes. Furthermore, a case study using panoramic videos converted from Google Street View images to simulate changing scenes was used to test the viability of this framework, resulting in five million big data points. The result of this study shows that through the collaboration of deep learning algorithms, finer visual variables were classified, subtle emotional responses were tracked, and better regression results for valence and arousal were obtained. Among all the predictors, the proportion of grass was the most significant predictor for emotional perception. The proposed framework is adaptable and human-centric, and it enables the instantaneous emotional perception of the built environment by the general public as a feedback survey tool to aid urban planners in creating UGS that promote emotional well-being.

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