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The aim of this study was to microencapsulate probiotic bacteria (Lactobacillus acidophilus 11073) using whey-protein-isolate (WPI)–octenyl-succinic-anhydride-starch (OSA-starch)-complex coacervates and to investigate the effects on probiotic bacterial viability during spray drying, simulated gastrointestinal digestion, thermal treatment and long-term storage. The optimum mixing ratio and pH for the preparation of WPI-OSA-starch-complex coacervates were determined to be 2:1 and 4.0, respectively. The combination of WPI and OSA starch under these conditions produced microcapsules with smoother surfaces and more compact structures than WPI-OSA starch alone, due to the electrostatic attraction between WPI and OSA starch. As a result, WPI-OSA-starch microcapsules showed significantly (p < 0.05) higher viability (95.94 ± 1.64%) after spray drying and significantly (p < 0.05) better protection during simulated gastrointestinal digestion, heating (65 °C/30 min and 75 °C/10 min) and storage (4/25 °C for 12 weeks) than WPI-OSA-starch microcapsules. These results demonstrated that WPI-OSA-starch-complex coacervates have excellent potential as a novel wall material for probiotic microencapsulation. [ABSTRACT FROM AUTHOR]

Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model. (2) To suppress irrelevant information, three SENet modules are introduced after the backbone to improve the ability of feature maps to express information, and CBAM and CA are introduced for comparison experiments. (3) A WIoU loss function based on a dynamic non-monotonic focusing mechanism is established to reduce the harmful gradients generated by low-quality examples on the original loss function and improve the model performance. During the model evaluation, the model is compared with other YOLO series and mainstream detection algorithms, and the model mAP@0.5 achieves 97.19%, which is 1.6% better than the YOLOv7 algorithm. In the model testing session, the root mean square error and coefficient of determination ( R 2 ) of YOLO-C are 1.88 and 0.96, respectively, indicating that YOLO-C has higher robustness and reliability for boll target detection in complex environments and can provide an effective method for yield prediction of long-staple cotton at maturity. [ABSTRACT FROM AUTHOR]

Climate plays an important role in heating energy consumption owing to the direct relationship between space heating and changes in meteorological conditions. To quantify the impact, the Transient System Simulation Program software was used to simulate the heating loads of office buildings in Harbin, Tianjin, and Shanghai, representing three major climate zones (i.e., severe cold, cold, and hot summer and cold winter climate zones) in China during 1961-2010. Stepwise multiple linear regression was performed to determine the key climatic parameters influencing heating energy consumption. The results showed that dry bulb temperature (DBT) is the dominant climatic parameter affecting building heating loads in all three climate zones across China during the heating period at daily, monthly, and yearly scales (R2 ≥ 0.86). With the continuous warming climate in winter over the past 50 years, heating loads decreased by 14.2, 7.2, and 7.1 W/m2 in Harbin, Tianjin, and Shanghai, respectively, indicating that the decreasing rate is more apparent in severe cold climate zone. When the DBT increases by 1 °C, the heating loads decrease by 253.1 W/m2 in Harbin, 177.2 W/m2 in Tianjin, and 126.4 W/m2 in Shanghai. These results suggest that the heating energy consumption can be well predicted by the regression models at different temporal scales in different climate conditions owing to the high determination coefficients. In addition, a greater decrease in heating energy consumption in northern severe cold and cold climate zones may efficiently promote the energy saving in these areas with high energy consumption for heating. Particularly, the likely future increase in temperatures should be considered in improving building energy efficiency. [ABSTRACT FROM AUTHOR]

ABSTRACT The present paper concerns the impact of climate on building heating energy consumption for different types of energy in Tianjin, a large city in northern China. The results show that heating energy consumption is dominantly related to dry bulb temperature ( DBT), but the impacts vary with energy types and time scales (day, month or year). DBT can explain 97.7% of the coal consumption at different time scales and 83.0-89.7% of the natural gas consumption. DBT can also account for 25% of the daily and monthly heating electricity consumption and 89% of the annual heating electricity consumption. These results suggest that different measures for building energy conservation should be taken because of the differing climate impacts for different energy types. Especially, likely increase in temperature in the future should be considered to improve the energy efficiency of buildings. In addition, different models should be used to predict building energy consumption at different time scales. This would be helpful for energy policy makers and energy providers for adjusting energy use strategies. [ABSTRACT FROM AUTHOR]

Recently, representation-based classifiers have gained increasing interest in hyperspectral image (HSI) classification. In this letter, based on our previously developed joint collaborative representation (JCR) classifier, an improved version, which is called weighted JCR (WJCR) classifier, is proposed. JCR adopts the same weights when extracting spatial and spectral features from surrounding pixels. Differing from JCR, WJCR attempts to utilize more appropriate weights by considering the similarity between the center pixel and its surroundings. Experimental results using two real HSIs demon strate that the proposed WJCR outperforms the original JCR and some other traditional classifiers, such as the support vector machine (SVM), the SVM with a composite kernel, and simultaneous orthogonal matching pursuit. [ABSTRACT FROM PUBLISHER]

Exploring changes of building energy consumption and its relationships with climate can provide basis for energy-saving and carbon emission reduction. Heating and cooling energy consumption of different types of buildings during 1981-2010 in Tianjin city, was simulated by using TRNSYS software. Daily or hourly extreme energy consumption was determined by percentile methods, and the climate impact on extreme energy consumption was analyzed. The results showed that days of extreme heating consumption showed apparent decrease during the recent 30 years for residential and large venue buildings, whereas days of extreme cooling consumption increased in large venue building. No significant variations were found for the days of extreme energy consumption for commercial building, although a decreasing trend in extreme heating energy consumption. Daily extreme energy consumption for large venue building had no relationship with climate parameters, whereas extreme energy consumption for commercial and residential buildings was related to various climate parameters. Further multiple regression analysis suggested heating energy consumption for commercial building was affected by maximum temperature, dry bulb temperature, solar radiation and minimum temperature, which together can explain 71.5 % of the variation of the daily extreme heating energy consumption. The daily extreme cooling energy consumption for commercial building was only related to the wet bulb temperature (R2= 0.382). The daily extreme heating energy consumption for residential building was affected by 4 climate parameters, but the dry bulb temperature had the main impact. The impacts of climate on hourly extreme heating energy consumption has a 1-3 hour delay in all three types of buildings, but no delay was found in the impacts of climate on hourly extreme cooling energy consumption for the selected buildings. [ABSTRACT FROM AUTHOR]

In this letter, kernel collaborative representation with Tikhonov regularization (KCRT) is proposed for hyperspectral image classification. The original data is projected into a high-dimensional kernel space by using a nonlinear mapping function to improve the class separability. Moreover, spatial information at neighboring locations is incorporated in the kernel space. Experimental results on two hyperspectral data prove that our proposed technique outperforms the traditional support vector machines with composite kernels and other state-of-the-art classifiers, such as kernel sparse representation classifier and kernel collaborative representation classifier. [ABSTRACT FROM PUBLISHER]

This paper aims at developing a mathematical model of hazard assessment of debris flow (DF) by applying hydrodynamics mechanism (HM) in the judging of DF starting. On the basis of HM, finite-volume method has been used and the flow passages have been classified into ground channel, river channel, and overflow channel; hence, the simulating result of depth distribution could be adopted to judge DF starting by comparing with DF critical depth. Meanwhile, rainfall, terrain, geology, vegetation, and population have been selected as the impact factors, and normalization method has been utilized to calculate the five factors' weights based on the synthesis of analytic hierarchy process and fuzzy-weighting method. Combining the impact factors' weights with its digitized results, respectively, thus, DF hazard assessment could be achieved with the programming calculation of Fortran. Indeed, the assessment results have been validated by comparing with the statistical result of geological hazards distribution in Guangxi and the Quanzhou County Debris Flow Event in 2011. The validation results show that the mathematical model is reliable, and it is feasible in the hazard assessment of DF. [ABSTRACT FROM AUTHOR]