With the rapid development of economy and technology, large-scale integrated energy buildings account for an increasing proportion of urban load. However, the randomness of EV owner behaviors, ...electricity price and outdoor temperature have brought challenges to the energy management of integrated energy buildings. This paper proposes a stochastic dynamic programming-based online algorithm to address the energy management of integrated energy buildings with electric vehicles and flexible thermal loads under multivariate uncertainties. First, an online energy management framework is established, which is further formulated as a multi-stage stochastic sequential decision-making problem. To address the complexities of the problem, a novel stochastic dynamic programming is employed to develop a distribution-free, computationally efficient, and scalable solution. By using extensive training samples, the algorithm is trained offline to learn how to deal with multivariate uncertainties and get the approximate optimal solution, which no longer depends on intraday forecast information. Numerical tests demonstrate the effectiveness of the proposed algorithm compared with other online algorithms in terms of optimality and computation efficiency.
The focus of students' health concerns has gradually progressed from the single factor of physical health to comprehensive health factors, and the physical and mental health of students are now ...generally considered together. This study focuses on exploring the status of junior high school students' physical health and their subjective health assessment with the major societal factors that affect students' lives: School Life and Family involvement. In addition, we explore the main factors influencing students' subjective health.
A cross-sectional survey was conducted with 190 Tibetan junior high school students in the Maozhuang Township. The intentional sampling was used to choose the research object. The structured questionnaire comprised four parts, namely social and demographic information, family condition, school life, and subjective health quality which was assessed by PROMIS (Chinese version of the Pediatric Patient-Reported Outcomes Measurement Information System).
The average height and weight of boys and girls are statistically different (p-values of 0.026 and 0.044, respectively), but there is no statistically significant difference in BMI (Body Mass Index) between boys and girls (p-value of 0.194). The average values of the five dimensions of depression, anger, anxiety, fatigue, and peer relationships in the PROMIS of the research subjects were 58.9 ± 5.3, 53.3 ± 8.0, 58.1 ± 7.3, 52.8 ± 8.0, 39.3 ± 6.6. In the demographic dimension, the grade was the main factor influencing anger (p < 0.01) and fatigue (p < 0.01), while gender was related to peer relationships (p = 0.02). In the family dimension, the father's educational level was related to peer relationships (p = 0.05), while the family financial situation was related to depression (p = 0.01). In the school life dimension, relationship with classmates was found to affect anger (p = 0.05), while homework was related to anxiety (p = 0.02) and fatigue (p = 0.05).
the physical health index BMI and subjective health evaluation of students are worse than students of more developed areas in China. Their family environment and school life all have varying degrees of impact on the five subjective health outcomes. There are differences in gender and grade level. The government and society need to pay more attention to the physical and mental health of students in remote and underdeveloped areas and improve their health through a student nutrition plan and the establishment of mental health offices.
In unsupervised person Re-ID, peer-teaching strategy leveraging two networks to facilitate training has been proven to be an effective method to deal with the pseudo label noise. However, training ...two networks with a set of noisy pseudo labels reduces the complementarity of the two networks and results in label noise accumulation. To handle this issue, this paper proposes a novel Dual Clustering Co-teaching (DCCT) approach. DCCT mainly exploits the features extracted by two networks to generate two sets of pseudo labels separately by clustering with different parameters. Each network is trained with the pseudo labels generated by its peer network, which can increase the complementarity of the two networks to reduce the impact of noises. Furthermore, we propose dual clustering with dynamic parameters (DCDP) to make the network adaptive and robust to dynamically changing clustering parameters. Moreover, Consistent Sample Mining (CSM) is proposed to find the samples with unchanged pseudo labels during training for potential noisy sample removal. Extensive experiments demonstrate the effectiveness of the proposed method, which outperforms the state-of-the-art unsupervised person Re-ID methods by a considerable margin and surpasses most methods utilizing camera information.
•Yarn supercapacitor was fabricated with PPy nanotube-coated cotton yarn electrodes.•Electrochemical performances of the yarn supercapacitor were investigated.•A high areal-specific capacitance of ...74.0mFcm−2 was achieved.
A novel all-solid-state yarn supercapacitor (YSC) has been fabricated by using the cotton yarns coated with polypyrrole (PPy) nanotubes. The interconnected network structure of PPy can increase the surface area as well as the electrode/electrolyte interface area, thus resulting in improved electrochemical performance. For the proposed YSC, a high areal-specific capacitance of 74.0mFcm−2 and a desirable energy density of 7.5μWhcm−2 are achieved. The flexibility of the YSC demonstrates that it is suitable for the integration as flexible power sources in wearable electronic textiles.
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•Image sequences of falling soybean seeds were obtained via dynamic capture.•An a priori clustering algorithm was proposed to segment touching seeds.•Forced nearest-neighbor data ...association algorithm tracked the motion of falling seeds.•Multi-view shape features were extracted to improve the recognition of broken seeds.
Seed counting and broken seed identification are important tasks in evaluating seed quality. In this study, we proposed a computational method designed to perform these two functions. Image sequences of soybean seeds during falling were collected, and their morphologies were examined from different views. An a priori clustering algorithm composed of a support vector machine and k-means clustering algorithm was used to segment touching seeds within images. The morphologies of specific soybean seeds in sequential images were associated based on a forced neighbor association criterion to avoid repeated counting and obtain shape features from multiple views. Based on the areas in different views, the basic shape features in the initial multi-view shape features were sorted to obtain the guided multi-view shape features. The support vector machine was used with the guided multi-view shape feature to classify seeds as intact or broken. The experimental results show that the proposed a priori clustering algorithm accurately segmented touching seeds. The forced nearest-neighbor data association algorithm is insensitive to touching seeds and achieved highly accurate seed counting. Compared with the single-view shape feature, the multi-view shape feature significantly improves the accuracy of seed morphological classification. The proposed method exhibited considerable potential for applications in agricultural engineering.
In most of the modern and developed industries, water is used as heat transfer fluid. However, the accumulation of many precipitates and suspended solids in some locations results in particulate ...fouling on the surface of heat exchangers. To anticipate the particulate fouling deposition at the specific location of the heat exchanger channel, an improved CFD particulate fouling model is proposed in this study, namely, the local fouling method. The method is applied to study the heat transfer surfaces of smooth and complex channels along with their particulate fouling deposition process. The results illustrate that the modeled fouling resistance of the two channels is in good agreement with the published experimental data. The average value of local fouling resistance obtained by the local method can substitute the integral fouling resistance obtained by the integral method. In addition, the fouling resistance data obtained by the local method approach has a stronger correlation with the experimental data as compared to that obtained by the existing integral method. And the local method may be applied to obtain local fouling information for both smooth and complex channels heat transfer surfaces.
In this study, the morphologies of the aggregate in multiple views were analysed during the falling of particles to calculate aggregate gradation. Four types of characterisation parameters were ...selected to extract the multi-view information of aggregate particles in five views. Based on the multi-view information, the aggregate particles were classified using principal component analysis and a probabilistic neural network. An aggregate equivalent volume characterisation method was formulated to calculate the aggregate mass, whereby the aggregate volume was converted into the aggregate mass by the least-squares method. The experimental results show that the proposed aggregate sieving method can effectively realise the gradation classification of aggregates. Considering the product of the maximum area and the minimum equivalent Feret ellipse minor axis as the equivalent volume, the calculated aggregate mass yielded a good correlation with the actual aggregate mass. Compared with single-view information, multi-view information can improve the accuracy and repeatability of gradation calculations. The use of multi-view information to calculate aggregate gradation can reduce manpower and improve detection efficiency, which is important for applications in the construction industry.
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•The morphologies of the falling aggregate were collected in multiple views.•The size information of aggregate in multiple views was fused using Principal Component Analysis.•The aggregate sieving was realized by Probabilistic Neural Network.•The characterisation method of aggregate equivalent volume was proposed.•The mapping relationship between the equivalent volume and the mass of aggregate was established by the least-squares method.
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•Accurately quantify phenolic acid aqueous solutions with aquaphotomics.•A novel spectral treatment method was used to amplify the spectral differences and reduce interference.•An ...aquaphotomics based NIR approach for analysis and monitoring of SAA transformation reactions at high temperature and high pressure.
As a fast and non-destructive detection method, near infrared spectroscopy, mainly containing overtones and combinations, can be used to quantify the components with a concentration of ≥ 1% in the analytical sample. Aquaphotomics uses the characteristic that the water structure changes with the addition of solute, which is reflected in the region of the water spectrum. Thus, it provides the possibility to unlock the information hidden in the spectrum. In our work, near infrared spectroscopy combined with aquaphotomics was used to quantify aqueous solution containing salvianolic acid B. It has shown that the aquaphotomics approach accurately quantifies the aqueous solution's salvianolic acid from 0.51 mg/mL to 25.86 mg/mL. The obtained RMSEP, R2, RPD, and MRE of prediction were 0.52 mg/mL, 0.995, 14.88 and 4.74%, respectively. For the salvianolic acid A reaction solution, the predicted R2 was 0.93, RMSEC was 0.85 mg/mL, and RMSEP was 0.82. The results of this study supported the concept of aquaphotomics, and the aquaphotomics approach was successfully applied in the reaction system of salvianolic acid A at 120 °C. This method was conducive to understanding the reaction and improving the accuracy of the quantitative model. It is a rapid and accurate alternative for analysis and measurement of transformation reactions at high temperature and high pressure, even for the substance with a concentration of less than 5 mg/mL.
Herbal medicines have played a vital role in maintaining the health of the world population in the past thousands of years, and have proved to be an effective therapy. It is important to improve our ...understanding of the effects of the multi-step processing in herbal medicines on the chemical changes to ensure product quality. A proton nuclear paramagnetic resonance (
H NMR)-based evaluation strategy was developed for an efficient process variation exploration and diversified metabolite identification. In this study, 48 process intermediates from 6 commercial batches of the multi-step manufacturing chain of Danshen processing were obtained. Hierarchical classification analysis (HCA) tree based on
H NMR spectra clustered the samples according to the processing steps, which indicates that
H NMR has the potential capability for critical control point identification based on its adequate information of the organic compounds. Then, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to distinguish the major metabolite differences between the intermediates before and after the critical control point. In this case, the alkali-isolation and acid-dissolution method was recognized as the most critical process in the multi-step chain of Danshen extract manufacturing. Potential metabolites with the larger amplitude of variation and contributing the most to the discrimination were found to be potential quality markers by
-plot, including several previously undetected amino acids. The results in this study are consistent with previous research studies and reference experiments conducted with other analytical tools. Taken together, they prove that
H NMR with chemometrics is a very effective process quality control tool to provide comprehensive information on the chemical changes during the processing of herbal medicines, and help with the identification of critical control points and potential critical quality markers.
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