Tangshan College

Lived experience is man’s experience of life, with main characteristics of authenticity, instantaneity, fluidity, integrity and non-mystique. The key of researching lived experience is to obtain lived experience. The process and step of researching lived experience have been pointed out, yet they are not ambulatory, they can be alternated or carried through at the same time. In order to inquiry lived experience well, such items should be paid attention to:the distinguish of fact and meaning, the researching opening attitude and the proper expression way. In addition, the difference between lived experience research and educational narrative inquiry should be regarded also.

The microwave absorption, electromagnetic interference shielding, and microwave response mechanism of graphene hybrids are highlighted, including relaxation, charge transport, magnetic resonance,<italic>etc</italic>.

With the development of parallel computing, distributed computing, grid computing, a new computing model appeared. The concept of computing comes from grid, public computing and SaaS. It is a new method that shares basic framework. The basic principles of cloud computing is to make the computing be assigned in a great number of distributed computers, rather then local computer or remoter server. The running of the enterprise's data center is just like Internet. This makes the enterprise use the resource in the application that is needed, and access computer and storage system according to the requirement. This article introduces the background and principle of cloud computing, the character, style and actuality. This article also introduces the application field the merit of cloud computing, such as, it do not need user's high level equipment, so it reduces the user's cost. It provides secure and dependable data storage center, so user needn't do the awful things such storing data and killing virus, this kind of task can be done by professionals. It can realize data share through different equipments. It analyses some questions and hidden troubles, and puts forward some solutions, and discusses the future of cloud computing. Cloud computing is a computing style that provide power referenced with IT as a service. Users can enjoy the service even he knows nothing about the technology of cloud computing and the professional knowledge in this field and the power to control it.

Many key aspects of society, such as transport, housing and health care, have been significantly improved by the advent of a range of electricity applications, and the power generation for electricity applications has been a major field of research. Photovoltaic (PV) self-powered technologies are promising technologies for addressing applications' power supply challenges and alleviating conventional electricity load and environmental pollution. This study reviews solar energy harvesting (SEH) technologies for PV self-powered applications. First, the PV power generation and scenarios of PV self-powered applications are analyzed. Second, analysis of system design for PV self-powered applications is presented. Third, key components for PV self-powered applications, including maximum power point tracking (MPPT) techniques and power management (PM) systems are discussed in detail. Furthermore, numerous PV self-powered applications and utilizations of energy harvesting are summarized. Finally, some recommendations are proposed for further research.

With the avalanche of DNA/RNA sequences generated in the post-genomic age, it is urgent to develop automated methods for analyzing the relationship between the sequences and their functions. Towards this goal, a series of sequence-based methods have been proposed and applied to analyze various character-unknown DNA/RNA sequences in order for in-depth understanding their action mechanisms and processes. Compared with the classical sequence-based methods, the pseudo nucleotide composition or PseKNC approach developed very recently has the following advantages: (1) it can convert length-different DNA/RNA sequences into dimension-fixed digital vectors that can be directly handled by all the existing machine-learning algorithms or operation engines; (2) it can contain the desired features and properties according to the selection or definition of users; (3) it can cover considerable sequence pattern information, both local and global. This minireview is focused on the concept of pseudo nucleotide composition, its development and applications.

Zinc ion batteries (ZIBs) have been gradually developed in recent years due to their abundant resources, low cost, and environmental friendliness. Therefore, ZIBs have received a great deal of attention from researchers, which are considered as the next generation of portable energy storage systems. However, poor overall performance of ZIBs restricts their development, which is attributed to zinc dendrites and a series of side reactions. Constructing 3D zinc anodes has proven to be an effective way to significantly improve their electrochemical performance. In this review, the challenges of zinc anodes in ZIBs, including zinc dendrites, hydrogen evolution and corrosion, as well as passivation, are comprehensively summarized and the energy storage mechanisms of the zinc anodes and 3D zinc anodes are discussed. 3D zinc anodes with different structures including fiberous, porous, ridge-like structures, plated zinc anodes on different substrates and other 3D zinc anodes, are subsequently discussed in detail. Finally, emerging opportunities and perspectives on the material design of 3D zinc anodes are highlighted and challenges that need to be solved in future practical applications are discussed, hopefully illuminating the way forward for the development of ZIBs.

The bacteriophage virion proteins play extremely important roles in the fate of host bacterial cells. Accurate identification of bacteriophage virion proteins is very important for understanding their functions and clarifying the lysis mechanism of bacterial cells. In this study, a new sequence-based method was developed to identify phage virion proteins. In the new method, the protein sequences were initially formulated by the g-gap dipeptide compositions. Subsequently, the analysis of variance (ANOVA) with incremental feature selection (IFS) was used to search for the optimal feature set. It was observed that, in jackknife cross-validation, the optimal feature set including 160 optimized features can produce the maximum accuracy of 85.02%. By performing feature analysis, we found that the correlation between two amino acids with one gap was more important than other correlations for phage virion protein prediction and that some of the 1-gap dipeptides were important and mainly contributed to the virion protein prediction. This analysis will provide novel insights into the function of phage virion proteins. On the basis of the proposed method, an online web-server, PVPred, was established and can be freely accessed from the website (http://lin.uestc.edu.cn/server/PVPred). We believe that the PVPred will become a powerful tool to study phage virion proteins and to guide the related experimental validations.

Our objective is to study the relationship between the regulatory proteins Bcl-2/Bax and mitochondria-mediated apoptosis during the differentiation of adipose-derived stromal cells (ADSCs) into neurons. Immunocytochemistry and western blotting showed that the cells weakly expressed neuron-specific enolase (NSE) in the non-induced group and expressed NSE more strongly in the groups induced for 1 h, 3 h, 5 h and 8 h. NSE expression peaked at 5 h (P < 0.05), although there was no significant difference between 5 and 8 h (P > 0.05). Bcl-2 expression gradually decreased over time in the non-induced group (P < 0.05). However, Bax, caspase-9, Cyt-c and caspase-3 expression gradually increased and peaked at 8 h (P < 0.05). Transmission electron microscopy revealed karyopyknosis, chromatin edge setting, mitochondria swelling and cavitation in cells at 5 h, and the mitochondrial membrane potential decreased over time, as demonstrated by laser scanning confocal microscopy. After a 5 h induction, cells differentiated into typical neurons and expressed Bcl-2, which inhibited apoptosis. Bax showed a strong apoptosis-promoting capacity, leading to changes in the mitochondrial membrane potential and structure, and then triggered the caspase-independent apoptotic response through the mitochondrial pathway. At the same time, Cyt-c was directly or indirectly released from the mitochondria to the cytoplasm to trigger the caspase-dependent apoptotic response through the mitochondrial pathway. Therefore, Bcl-2/Bax play an important role in regulating caspase-dependent and caspase-independent apoptosis mediated by the mitochondrial pathway during the differentiation of ADSCs into neurons.

Six d¹⁰metal coordination polymers have been prepared and characterized. Complexes possess structural diversities with interesting topologies and high catalytic activities for the degradation of methyl orange.

Immunoglobulins, also called antibodies, are a group of cell surface proteins which are produced by the immune system in response to the presence of a foreign substance (called antigen). They play key roles in many medical, diagnostic and biotechnological applications. Correct identification of immunoglobulins is crucial to the comprehension of humoral immune function. With the avalanche of protein sequences identified in postgenomic age, it is highly desirable to develop computational methods to timely identify immunoglobulins. In view of this, we designed a predictor called "IGPred" by formulating protein sequences with the pseudo amino acid composition into which nine physiochemical properties of amino acids were incorporated. Jackknife cross-validated results showed that 96.3% of immunoglobulins and 97.5% of non-immunoglobulins can be correctly predicted, indicating that IGPred holds very high potential to become a useful tool for antibody analysis. For the convenience of most experimental scientists, a web-server for IGPred was established at http://lin.uestc.edu.cn/server/IGPred. We believe that the web-server will become a powerful tool to study immunoglobulins and to guide related experimental validations.

Automatic defect detection on the steel surface is a challenging task in computer vision, owing to miscellaneous patterns of the defects, low contrast between the defect and background, the existence of pseudo defects, and so on. In this paper, a new Haar-Weibull-variance (HWV) model is proposed for steel surface defect detection in an unsupervised manner. First, an anisotropic diffusion model is utilized to eliminate the influence of pseudodefects. Second, a new HWV model is established to characterize the texture distribution of each local patch in the image. The proposed model can project the texture distribution of each patch into the low-dimensional space with only two parameters. The parameter distribution of the whole image can also be unified into the form of linear radiation in an Euclidean space. The reliable background can be extracted via the formation of parameter distribution, by which the model parameter can be optimized further. Finally, the adaptive threshold can be determined to distinguish the defect from the background. Experimental results show that the proposed method can detect an arbitrary type of defect on the homogeneously textured surface and achieve an average detection rate of 96.2% on the data set, which outperforms the previous methods.

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing semantic information for downstream tasks, e.g., classification. Hence, a model’s representation learning ability is critical for enhancing its performance. This article proposes a densely knowledge-aware network (DKN) for MTSC. The DKN’s feature extractor consists of a residual multihead convolutional network (ResMulti) and a transformer-based network (Trans), called ResMulti-Trans. ResMulti has five residual multihead blocks for capturing the local patterns of data while Trans has three transformer blocks for extracting the global patterns of data. Besides, to enable dense mutual supervision between lower-and higher-level semantic information, this article adapts densely dual self-distillation (DDSD) for mining rich regularizations and relationships hidden in the data. Experimental results show that compared with 5 state-of-the-art self-distillation variants, the proposed DDSD obtains 13/4/13 in terms of “win”/“tie”/“lose” and gains the lowest-AVG_rank score. In particular, compared with pure ResMulti-Trans, DKN results in 20/1/9 regarding win/tie/lose. Last but not least, DKN overweighs 18 existing MTSC algorithms on 10 UEA2018 datasets and achieves the lowest-AVG_rank score.

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher levels. Low-level semantic information is essential and should be considered in the CL process. However, the existing CL algorithms mainly focus on the similarity of high-level semantic information. Considering the similarity of low-level semantic information may improve the performance of CL. To this end, we present a deep contrastive representation learning with self-distillation (DCRLS) for the time series domain. DCRLS gracefully combine data augmentation, deep contrastive learning, and self distillation. Our data augmentation provides different views from the same sample as the input of DCRLS. Unlike most CL algorithms that concentrate on high-level semantic information only, our deep contrastive learning also considers the contrast similarity of low-level semantic information between peer residual blocks. Our self distillation promotes knowledge flow from high-level to low-level blocks to help regularize DCRLS in the knowledge transfer process. The experimental results demonstrate that the DCRLS-based structures achieve excellent performance on classification and clustering on 36 UCR2018 datasets.

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called CapMatch. CapMatch gracefully hybridizes supervised learning and unsupervised learning to extract rich representations from input data. In unsupervised learning, CapMatch leverages the pseudolabeling, contrastive learning (CL), and feature-based KD techniques to construct similarity learning on lower and higher level semantic information extracted from two augmentation versions of the data, "weak" and "timecut," to recognize the relationships among the obtained features of classes in the unlabeled data. CapMatch combines the outputs of the weak- and timecut-augmented models to form pseudolabeling and thus CL. Meanwhile, CapMatch uses the feature-based KD to transfer knowledge from the intermediate layers of the weak-augmented model to those of the timecut-augmented model. To effectively capture both local and global patterns of HAR data, we design a capsule transformer network consisting of four capsule-based transformer blocks and one routing layer. Experimental results show that compared with a number of state-of-the-art semi-supervised and supervised algorithms, the proposed CapMatch achieves decent performance on three commonly used HAR datasets, namely, HAPT, WISDM, and UCI_HAR. With only 10% of data labeled, CapMatch achieves values of higher than 85.00% on these datasets, outperforming 14 semi-supervised algorithms. When the proportion of labeled data reaches 30%, CapMatch obtains values of no lower than 88.00% on the datasets above, which is better than several classical supervised algorithms, e.g., decision tree and -nearest neighbor (KNN).

Novel core–shell structured ellipsoid-like BiVO₄@g-C₃N₄ composites, with different amounts of g-C₃N₄, have been successfully prepared by a simple hydrothermal-chemisorption method.

A new clustering routing method based on predictive energy consumption efficiency (PECE) for a wireless sensor network (WSN) is presented in this paper. It consists of two stages: cluster formation and stable data transfer. In the cluster formation stage, we design an energy-saving clustering routing algorithm based on the node degree, the relative distance between nodes, and the rest energy of nodes. When this algorithm selects the cluster head, the node degree and the relative distance between the nodes are fully considered, so the selected cluster not only has better coverage performance but also short average distance from other member nodes in the formative cluster; therefore, the cost of communications within the clusters is small. In the stable data transfer stage, by using bee colony optimization (BCO), we design a PECE strategy for data transmission. On the basis of considering the predictive values of energy consumption, the hops, and the propagation delay on this route, this strategy gives a precise definition of the route yield by using two types of bee agent to predict the route yield of each routing path from the source node to the sink node. Through the optimization design of the algorithm, it can improve the quality of clusters, thereby increasing the overall network performance, and reduces and balances the energy consumption of whole network and prolongs the survival time of the network.

A new microemulsions system of curcumin (CUR-MEs) was successfully developed to improve the solubility and bioavailability of curcumin. Several formulations of the microemulsions system were prepared and evaluated using different ratios of oils, surfactants, and co-surfactants (S&CoS). The optimal formulation, which consists of Capryol 90 (oil), Cremophor RH40 (surfactant), and Transcutol P aqueous solution (co-surfactant), could enhance the solubility of curcumin up to 32.5 mg/mL. The pharmacokinetic study of microemulsions was performed in rats compared to the corresponding suspension. The stability of microemulsions after dilution was excellence. Microemulsions have significantly increased the C(max) and area under the curve (AUC) in comparison to that in suspension (p < 0.05). The relative bioavailability of curcumin in microemulsions was 22.6-fold higher than that in suspension. The results indicated that the CUR-MEs could be used as an effective formulation for enhancing the oral bioavailability of curcumin.

Mycobacterium tuberculosis is a bacterium that causes tuberculosis, one of the most prevalent infectious diseases. Predicting the subcellular localization of mycobacterial proteins in this bacterium may provide vital clues for the prediction of protein function as well as for drug discovery and design. Therefore, a computational method that can predict the subcellular localization of mycobacterial proteins with high precision is highly desirable. We propose a computational method to predict the subcellular localization of mycobacterial proteins. An objective and strict benchmark dataset was constructed after collecting 272 non-redundant proteins from the universal protein resource (the UniProt database). Subsequently, a novel feature selection strategy based on binomial distribution was used to optimize the feature vector. Finally, a subset containing 219 chosen tripeptide features was imported into a support vector machine-based method to estimate the performance of the dataset in accurately and sensitively identifying these proteins. We found that the proposed method gave a maximum overall accuracy of 89.71% with an average accuracy of 81.12% in the jackknife cross-validation. The results indicate that our prediction method gave an efficient and powerful performance when compared with other published methods. We made the proposed method available on a purpose built Web server called MycoSub that is freely accessible at . We anticipate that MycoSub will become a useful tool for studying the functions of mycobacterial proteins and for designing and developing anti-mycobacterium drugs.

We present a simple and convenient way to engineer the porosity of NOPs utilizing two crosslinkers with different length and various types of building blocks. The obtained polymers display hierarchical pore structures, remarkablely high CO₂ uptake capacities and sorption selectivity for CO₂/N₂.

Two water-stable cadmium(<sc>ii</sc>) MOFs were synthesized and characterized. <bold>1</bold> is the first dual-function Cd(<sc>ii</sc>)-MOF luminescent sensor for sensing acetylacetone and Cr₂O₇²⁻ in aqueous solution with high sensitivity and selectivity and good recyclability.