Mpemba Effect in Crystallization of Polybutene-1

The Mpemba effect and its inverse can be understood as a result of nonequilibrium thermodynamics. In polymers, changes of state are generally non-equilibrium processes. However, the Mpemba effect has been rarely reported in the crystallization of polymers. In the melt, polybutene-1 (PB-1) has the lowest critical cooling rate in polyolefins and tends to maintain its original structure and properties with thermal history. A nascent PB-1 sample was prepared by using metallocene catalysis at low temperature, and the crystallization behavior and crystalline structure of the PB-1 were characterized by DSC and WAXS. Experimentally, a clear Mpemba effect is observed not only in the crystallization of the nascent PB-1 melt in form II but also in form I obtained from the nascent PB-1 at low melting temperature. It is proposed that this is due to the differences in the chain conformational entropy in the lattice which influence conformational relaxation times. The entropy and the relaxation time can be predicted using the Adam-Gibbs equations, whereas non-equilibrium thermodynamics is required to describe the crystallization with the Mpemba effect

Chilling Stress—The Key Predisposing Factor for Causing Alternaria alternata Infection and Leading to Cotton (Gossypium hirsutum L.) Leaf Senescence

Leaf senescence plays a vital role in nutrient recycling and overall capacity to assimilate carbon dioxide. Cotton premature leaf senescence, often accompanied with unexpected short-term low temperature, has been occurring with an increasing frequency in many cotton-growing areas and causes serious reduction in yield and quality of cotton. The key factors for causing and promoting cotton premature leaf senescence are still unclear. In this case, the relationship between the pre-chilling stress and Alternaria alternata infection for causing cotton leaf senescence was investigated under precisely controlled laboratory conditions with four to five leaves stage cotton plants. The results showed short-term chilling stress could cause a certain degree of physiological impairment to cotton leaves, which could be recovered to normal levels in 2–4 days when the chilling stresses were removed. When these chilling stress injured leaves were further inoculated with A. alternata, the pronounced appearance and development of leaf spot disease, and eventually the pronounced symptoms of leaf senescence, occurred on these cotton leaves. The onset of cotton leaf senescence at this condition was also reflected in various physiological indexes such as irreversible increase in malondialdehyde (MDA) content and electrolyte leakage, irreversible decrease in soluble protein content and chlorophyll content, and irreversible damage in leaves' photosynthesis ability. The presented results demonstrated that chilling stress acted as the key predisposing factor for causing A. alternata infection and leading to cotton leaf senescence. It could be expected that the understanding of the key factors causing and promoting cotton leaf senescence would be helpful for taking appropriate management steps to prevent cotton premature leaf senescence

Crystalline structures and crystallization behaviors of poly(L-lactide) in poly(L-lactide)/graphene nanosheet composites

GNS existence in PLLA favors α′ crystal formation more than α crystal formation resulting in a shift of α′–α crystal formation transition toward high Tcs.</p

A novel prediction method for protein DNA-binding residues based on neighboring residue correlations

AbstractAccurately identifying the protein DNA-binding residues is important for understanding the protein–DNA recognition mechanism and protein function annotation. Many computational methods have been proposed to predict protein–DNA binding residues using protein sequence information; however, for severe imbalanced data like the protein–DNA binding dataset, the under-sampling technique which is applied by most previous methods cannot achieve satisfactory performance. In this study, an adjustment algorithm is proposed to offset the biased prediction results from the classifier. The proposed adjustment algorithm uses the binding probability between the target residue and its neighboring residues to identify more true binding residues which are wrongly predicted as non-binding. The proposed prediction method with adjustment algorithm achieves an area under the ROC curve (AUC) of 0.926 and 0.866 on two benchmark datasets and 0.882 on the independent testing set, which demonstrates that the proposed method can efficiently predict specific residues for protein–DNA interactions

An Imbalanced Data Classification Algorithm of De-noising Auto-Encoder Neural Network Based on SMOTE

Imbalanced data classification problem has always been one of the hot issues in the field of machine learning. Synthetic minority over-sampling technique (SMOTE) is a classical approach to balance datasets, but it may give rise to such problem as noise. Stacked De-noising Auto-Encoder neural network (SDAE), can effectively reduce data redundancy and noise through unsupervised layer-wise greedy learning. Aiming at the shortcomings of SMOTE algorithm when synthesizing new minority class samples, the paper proposed a Stacked De-noising Auto-Encoder neural network algorithm based on SMOTE, SMOTE-SDAE, which is aimed to deal with imbalanced data classification. The proposed algorithm is not only able to synthesize new minority class samples, but it also can de-noise and classify the sampled data. Experimental results show that compared with traditional algorithms, SMOTE-SDAE significantly improves the minority class classification accuracy of the imbalanced datasets

Solving the Fragment Complexity of Official, Social, and Sensorial Urban Data

Cities in the big data era hold the massive urban data to create valuable information and digitally enhanced services. Sources of urban data are generally categorized as one of the three types: official, social, and sensorial, which are from the government and enterprises, social networks of citizens, and the sensor network. These types typically differ significantly from each other but are consolidated together for the smart urban services. Based on the sophisticated consolidation approaches, we argue that a new challenge, fragment complexity that represents a well-integrated data has appropriate but fragmentary schema and difficult to be queried, is ignored in the state-of-art urban data management. Comparing with predefined and rigid schema, fragmentary schema means a dataset contains millions of attributes but nonorthogonally distributed among tables, and of course, values of these attributes are even massive. As far as a query is concerned, locating where these attributes are being stored is the first encountered problem, while traditional value-based query optimization has no contributions. To address this problem, we propose an index on massive attributes as an attributes-oriented optimization, namely, attribute index. Attribute index is a secondary index for locating files in which the target attributes are stored. It contains three parts: ATree for searching keys, DTree for locating keys among files, and ADLinks as a mapping table between ATree and DTree. In this paper, the index architecture, logical structure and algorithms, the implementation details, the creation process, the integration to the existing key-value store, and the urban application scenario are described. Experiments show that, in comparison with B + -Tree, LSM-Tree, and AVL-Tree, the query time of ATree is 1.1x, 1.5x, and 1.2x faster, respectively. Finally, we integrate our proposition with HBase, namely, UrbanBase, whose query performance is 1.3x faster than the original HBase

Robot Path Planning Method Based on Improved Genetic Algorithm

This paper presents an improved genetic algorithm for mobile robot path planning. The algorithm uses artificial potential method to establish the initial population, and increases value weights in the fitness function, which increases the controllability of robot path length and path smoothness. In the new algorithm, a flip mutation operator is added, which ensures the individual population collision path. Simulation results show that the proposed algorithm can get a smooth, collision-free path to the global optimum, the path planning algorithm which is used to solve the problem is effective and feasible

Machine Learning-Based Keywords Extraction for Scientific Literature

Abstract: With the currently growing interest in the Semantic Web, keywords/metadata extraction is coming to play an increasingly important role. Keywords extraction from documents is a complex task in natural languages processing. Ideally this task concerns sophisticated semantic analysis. However, the complexity of the problem makes 1472 current semantic analysis techniques insufficient. Machine learning methods can support the initial phases of keywords extraction and can thus improve the input to further semantic analysis phases. In this paper we propose a machine learning-based keywords extraction for given documents domain, namely scientific literature. More specifically, the least square support vector machine is used as a machine learning method. The proposed method takes the advantages of machine learning techniques and moves the complexity of the task to the process of learning from appropriate samples obtaine