Isolation and identification of Lactobacillus and yeast species and their effect on the quality of fermented rice cakes

In this study, microbes were isolated from the rice slurry of a fermented rice cake to obtain lactic acid bacteria and yeast species. These species were identified using microbial physiology and gene sequence analyses. As the growth of the lactic acid bacterial strain R-2b and the yeast J-3a strains were found to be the best, a composite starter comprising these microbes was used for the preparation of fermented rice cakes. Based on single factor and orthogonal experiments, when the proportion of Lactobacillus plantarum, Saccharomyces cerevisiae, and Candida humilis was 1:3:6, the optimal fermentation conditions were addition of sugar and starter amounts of 20% and 6%, respectively, a fermentation temperature of 32 °C, and fermentation time of 8 h. The fermented rice cake with this optimum ratio had the most abundant volatile components and qualified physicochemical and microbial indexes. Additionally, the overall quality was better than that of commercially available products.     

基于并行跟踪检测框架与深度学习的目标跟踪算法

在空地协同背景下，地面目标的移动导致其在无人机视角下外观会发生较大变化，传统算法很难满足此类场景的应用要求。针对这一问题，提出基于并行跟踪和检测（PTAD）框架与深度学习的目标检测与跟踪算法。首先，将基于卷积神经网络（CNN）的目标检测算法SSD作为PTAD的检测子处理关键帧获取目标信息并提供给跟踪子；其次，检测子与跟踪子并行处理图像帧并计算检测与跟踪结果框的重叠度及跟踪结果的置信度；最后，根据跟踪子与检测子的跟踪或检测状态来判断是否对跟踪子或检测子进行更新，并对图像帧中的目标进行实时跟踪。在无人机视角下的视频序列上开展实验研究和对比分析，结果表明所提算法的性能高于PTAD框架下最优算法，而且实时性提高了13%，验证了此算法的有效性。     

Application of fuzzy image restoration in criminal investigation

The advancement of science and technology has a positive effect on the development of law disciplines. The development of algorithms and artificial intelligence also has a certain impact on judicial practice. Image restoration is a significant technique in image processing. It aims to objectively restore the content or quality of the original image from the degraded image. Image degradation is always generated in image transmission, such as distortion, blur. In modern video surveillance system, image restoration is significant for criminal investigation. However, image restoration based on conventional filter algorithms cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal investigation. We conduct experiments on both degraded images and videos and experimental results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

How the intensity of the immersive experience impels the extent of Social Streaming App dependency? An empirical assessment with mediation and moderation effects

With the rapid growth and popularity of Social Streaming Apps (SSAs) worldwide, it is valuable for scholars to understand the links between immersive experience provided by SSA, users’ attitudes towards SSA, and the resultant SSA dependency. Although unexplored in the SSA context, previous research has indicated the significance of immersive experience and its influence on user’s specific behavior through user attitude. The present study employs Structural Equation Modelling, by using 459 samples from a survey, to explore how the intensity of immersive experience can influence the degree of user dependency on SSA. Results indicate that immersive experience is positively associated with users’ positive attitudes towards SSA, which would lead to SSA dependency. In addition, this study also found out that users’ favorable attitude partially mediates the relationship between immersive experience and solitary play dependency. Whereas, attitude fully mediated the relationship between five out of six dimensions of SSA dependency. Furthermore, to better comprehend the intervening relationships between immersive experiences, user attitude, SSA dependency, the study also conducted a moderation analysis taking frequency of use as a moderating variable. The results illustrate that a high frequency of use is a strong psychological indicator for SSA dependency. This supports the importance of considering actual behavior along with their psychological states when examining users’ immersive experience and their SSA dependency.     

Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.     

Analysis and control of micro-stepping characteristics of ultrasonic motor

Frontiers of Mechanical Engineering - Micro-stepping motion of ultrasonic motors satisfies biomedical applications, such as cell operation and nuclear magnetic resonance, which require a precise...     

Thermal annealing characteristics of solar selective absorber coatings based on nano-multilayered MoOx films

Designing double metal-dielectric (cermet) solar selective absorber coatings (SSACs) often requires complex co-sputtering techniques with multiple targets. This inevitably limits the simple and low-cost industrial fabrication. Here, we develop novel nano-multilayered MoO_x-based SSACs by simple and stable one-step reactive magnetron sputtering process using single molybdenum target. The proposed multilayer SSACs exhibit good solar absorptance of 0.93 and low thermal emittance of 0.06. Owing to the temperature-induced oxygen diffusion and oxidation phenomenon the as-sputtered SSACs have a poor thermal tolerance under air atmosphere, and after annealing at 200 °C for 150 h, the resulting absorptance is diminished from 0.93 to 0.90. However, the optical performance of the annealed SSAC is relatively stable in high-vacuum environment, even after annealing at 450 °C for 200 h, it still displays an ideal spectral selectivity of 0.92/0.07. With above properties, the resulting MoO_x-based SSAC is a promising absorber for enduring thermal harvesting in solar vacuum collectors.     

The relationship between enhanced dielectric property and structural distortion in Ca doped Ba2NaNb5O15 tungsten bronze ceramics

The relationship between enhanced dielectric property and structural distortion in tungsten bronze structure ceramics was discussed in this work. The ceramics with the composition of (Ba_1-xCa_x)₂NaNb₅O₁₅ (x = 0, 0.1, 0.2, 0.3, 0.4) were fabricated via conventional solid-state method. All ceramics were pure without secondary phase and the distinct lattice distortion in structure was testified by Rietveld XRD refinement. Compared with the un-doped composition, the maximum polarization and energy storage density were strongly enhanced according to the ferroelectric property measurements, which were contributed to the distortion of NbO₆ octahedron induced by the variation of ionic radius. The actually pulsed charge-discharge property of x = 0.3 ceramic was tested, whist excellent power density (P_D = 35.106 MV/cm³) and discharge energy density (W_d = 0.29 J/cm³) were obtained at 100 °C under 120 kV/cm, revealing the potential for application of Ba₂NaNb₅O₁₅ system-based ceramics in harsh environment.