Search

Importance Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings. Objectives To systematically explore the applications of RL in NLP, focusing on its effectiveness in acquiring optimal strategies, particularly in healthcare settings, and provide a comprehensive understanding of RL's potential in NLP tasks. Materials and Methods Adhering to the PRISMA guidelines, an exhaustive literature review was conducted to identify instances where RL has exhibited success in NLP applications, encompassing dialogue systems, machine translation, question-answering, text summarization, and information extraction. Our methodological approach involves closely examining the technical aspects of RL methodologies employed in these applications, analyzing algorithms, states, rewards, actions, datasets, and encoder-decoder architectures. Results The review of 93 papers yields insights into RL algorithms, prevalent techniques, emergent trends, and the fusion of RL methods in NLP healthcare applications. It clarifies the strategic approaches employed, datasets utilized, and the dynamic terrain of RL-NLP systems, thereby offering a roadmap for research and development in RL and machine learning techniques in healthcare. The review also addresses ethical concerns to ensure equity, transparency, and accountability in the evolution and application of RL-based NLP technologies, particularly within sensitive domains such as healthcare. Discussion The findings underscore the promising role of RL in advancing NLP applications, particularly in healthcare, where its potential to optimize decision-making and enhance patient outcomes is significant. However, the ethical challenges and technical complexities associated with RL demand careful consideration and ongoing research to ensure responsible and effective implementation. Conclusions By systematically exploring RL's applications in NLP and providing insights into technical analysis, ethical implications, and potential advancements, this review contributes to a deeper understanding of RL's role for language processing. [ABSTRACT FROM AUTHOR]

Objective To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness. Materials and Methods Our study was conducted in two phases. Initially, we analyzed a range of demonstration components vital for LLMs' biomedical data capabilities, including task descriptions and examples, experimenting with various combinations. Subsequently, we introduced the LLM instruction-example adaptive prompting (LEAP) framework, including instruction adaptive tuning, example adaptive tuning, and instruction-example adaptive tuning methods. This framework aims to systematically investigate both adaptive task descriptions and adaptive examples within the demonstration. We assessed the performance of the LEAP framework on the DDI, ChemProt, and BioRED datasets, employing LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B. Results Our findings indicated that Instruction + Options + Example and its expanded form substantially improved F1 scores over the standard Instruction + Options mode for zero-shot LLMs. The LEAP framework, particularly through its example adaptive prompting, demonstrated superior performance over conventional instruction tuning across all models. Notably, the MedLLAMA_13B model achieved an exceptional F1 score of 95.13 on the ChemProt dataset using this method. Significant improvements were also observed in the DDI 2013 and BioRED datasets, confirming the method's robustness in sophisticated data extraction scenarios. Conclusion The LEAP framework offers a compelling strategy for enhancing LLM training strategies, steering away from extensive fine-tuning towards more dynamic and contextually enriched prompting methodologies, showcasing in biomedical relation extraction. [ABSTRACT FROM AUTHOR]

Objectives This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework. Materials and Methods Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance. Results Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance. Discussion This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study. Conclusion The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks. [ABSTRACT FROM AUTHOR]

The multiple regeneration of aging asphalt pavements has emerged as a pavement maintenance approach. However, the micromechanisms and performance deterioration patterns associated with multiple regenerations remain unclear. The present work aims to investigate the rheological and physicochemical properties of styrenic block copolymers (SBS)-modified asphalt during multiple recycling processes. The rheological properties, nanostructure, and functional group evolution of SBS-modified asphalt during multiple recyclings were evaluated using rotational viscosity, frequency scanning, multistress creep recovery (MSCR), atomic force microscopy (AFM), and Fourier transform infrared (FTIR) tests. The key findings demonstrated that the viscoelasticity of asphalt is affected by both SBS degradation and asphalt aging. During the initial aging phase, SBS degradation mainly affects the asphalt properties, whereas the subsequent aging process becomes the dominant factor. Moreover, the viscoelasticity of the asphalt binder is primarily influenced by aging rather than the SBS degradation. Importantly, the SBS-modified asphalt continues to meet specification requirements even after multiple recycling rounds, demonstrating the feasibility of aging SBS-modified asphalt binder through multiple regeneration techniques. [ABSTRACT FROM AUTHOR]

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining. Scientific contribution: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance. [ABSTRACT FROM AUTHOR]

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering. In this work, the authors proposed a few-shot learning approach that can efficiently optimize protein language models for fitness prediction. It combines the techniques of meta-transfer learning, learning to rank, and parameter-efficient fine-tuning. [ABSTRACT FROM AUTHOR]

The paper provides a review of mix design, construction temperature determination, performance and life-cycle assessment (LCA) of WMA mixtures. The mix design of WMA mixtures using almost the same design protocols as the HMA mixtures. The techniques used for determining WMA mixtures' construction temperatures include the analogy, phase angle, compaction energy index (CEI) and traffic densification index (TDI), and 4% air void methods. The use of Sasobit is advisable for the road prone to rutting. For the road prone to water damage, the use of foaming technologyis not suitable and Rediset is recommended. For the road prone to fatigue failure, the use of Water-based processes is not recommended, while Evotherm and Rediset are advisable. From an environmental point of view, water-based processes are recommended for the production of WMA mixtures, followed by organic additives and chemical additives, whereas the use of water-bearing additives is not recommended. [ABSTRACT FROM AUTHOR]

Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making. [ABSTRACT FROM AUTHOR]

Tourism managers and practitioners rely on accurate demand forecasting and well-informed management guidance. Given the pandemic's consequences on tourism, future analysis in the during-epidemic era is urgently needed. This study aims to achieve three goals combining the utilization of decomposition algorithms and deep learning models: 1) to investigate the changes in tourism demand according to seasonal fluctuations of various frequencies, 2) to improve modelling accuracy in tourism demand forecasting during non-crisis periods and in the peri-COVID-19 era, and 3) to analyze tourism demand evolution in the peri-Covid-19 era. The volume of domestic tourism in Hawaii is used as sample data for demonstration and validation. The empirical findings demonstrate that the framework provided in this study has excellent interpretability and forecasting accuracy, surpasses all benchmark models in terms of error calculation and statistical tests and can provide further insights into peri-Covid-19 demand analysis and management. [ABSTRACT FROM AUTHOR]

The number of studies considering building performance optimization (BPO) in the building design phase is steadily growing, but many of the existing studies do not consider the applicability of many-objective optimization algorithms when increasing the objective dimensions. This article first compares the NSGA-II, IDBEA, MSOPS-II, and NSGA-III algorithms. Then, the algorithm most suitable for many-objective optimization is combined with Artificial natural network(ANN) and TOPSIS-AHP to complete the optimization of four dimensions of building energy consumption (EC), useful daylight illuminance (UDI), comfort time ratio (CTR) and energy-saving envelope cost (ESEC) for village houses in cold regions of China. The results show that the NSGA-III algorithm performs well in terms of convergence speed, convergence, diversity, and uniformity when solving many-objective problems compared to the other three algorithms. Finally, four optimization strategies were selected using the TOPSIS-AHP method. [ABSTRACT FROM AUTHOR]

High-viscosity modified bitumen is affected by a complex thermal oxygen environment during long-term service. However, the existing standard long-term thermal oxygen aging test cannot fully simulate the effect of different thermal oxygen conditions on the aging of high-viscosity modified bitumen. In this study, on the basis of the standard pressure aging vessel test, high-viscosity modified bitumen was aged under different oxygen conditions through adjusting test parameters. Then, the analysis of the complex moduli, phase angles, and creep and recovery properties was conducted to evaluate the rheological properties of high-viscosity modified bitumen before and after aging. Moreover, gel permeation chromatography was performed to evaluate the molecular size distribution of high-viscosity modifiers during aging. The results indicate that aging improves the modulus of high-viscosity modified bitumen and changes the phase angle of that. Temperature, pressure, and time are the factors affecting the high-temperature sensitivity and viscoelastic properties of high-viscosity modified bitumen. With respect to the creep and recovery property, different high-viscosity modified bitumen exhibits different aging characteristics with the change of thermal oxygen conditions. Gel-permeation-chromatography results directly illustrate that thermal oxygen conditions influence the degradation of high-viscosity modifiers at the initial stage of long-term aging, which is the key factor affecting the rheological properties of high-viscosity modified bitumen. [ABSTRACT FROM AUTHOR]

The reuse of recycled waste plastics has long been attempted in pavement engineering as bitumen modifier. It was revealed that waste plastics can significantly enhance the high-temperature performance of bitumen and bitumen mixtures. Even so, the application of waste plastics as a bitumen modifier is still not widespread. This is attributable to the generally poor low-temperature performance of plastic-modified bitumen, which often fails to meet specification requirements. For this purpose, styrene butadiene rubber (SBR) was selected to improve the low-temperature performance of plastic-modified bitumen. However, due to the long-term aging process, the composite and structure of the modified bitumen will change, which negatively impacts its performance. The objective of this study is to investigate the long-term aging behavior of plastic/SBR composite-modified bitumen. For this purpose, waste polyethylene was used as a plastic modifier and was mixed with base bitumen and 3% SBR at ratios 4.5%, 6% and 7.5%. The rheological properties and molecular weight distribution of base bitumen, plastic and plastic/SBR-modified bitumen before and after long-term aging were measured. Results show that the incorporation of plastic can improve the complex modulus, rutting factor and percent recovery of bitumen and reduce the non-recoverable creep compliance of the bitumen, indicating the modification process enhances the high-temperature performance of bitumen. The enhancement effect is more pronounced with the increase of plastic content. For modified bitumen with 7.5% plastic modifier, the complex modulus of modified bitumen is increased by 1127.55% compared to base bitumen. The addition of 3% SBR modifier can further improve the high-temperature performance of the modified bitumen. In addition, the modification process also increases the large molecule size percentage (LMSP) and weight average molecular weight of bitumen. Compared with weight average molecular weight, the LMSP correlates well with the rheological properties of modified bitumen. In accordance with the complex modulus, using the LMSP and weight average molecular weight of bitumen before and after aging, the corresponding aging index was calculated. The quantitative results showed that the addition of plastic modifier can improve the aging resistance of bitumen, but the enhancement effect is not as obvious as that of SBR modifier. [ABSTRACT FROM AUTHOR]

Due to the highly viscous characteristics of high viscosity modified bitumen (HVMB), the commonly used short-term aging schemes are not suitable for it. As such, the objective of this study is to introduce a suitable short-term aging scheme for HVMB by increasing the aging period and temperature. For this purpose, two kinds of commercial HVMB were aged via rolling thin-film oven test (RTFOT) and thin-film oven test (TFOT) at different aging periods and temperatures. At the same time, open-graded friction course (OGFC) mixtures prepared using HVMB were also aged via two aging schemes to simulate the short-term aging of bitumen at the mixing plant. With the aid of temperature sweep, frequency sweep, and multiple stress creep recovery tests, the rheological properties of short-term aged bitumen and the extracted bitumen were tested. By comparing the rheological properties of TFOT- and RTFOT-aged bitumen with those of extracted bitumen, suitable laboratory short-term aging schemes for HVMB were determined. Comparative results showed that aging the OGFC mixture in a 175 °C forced-draft oven for 2 h is suitable to simulate the short-term aging process of bitumen at the mixing plant. Compared with RTOFT, TFOT was more preferable for HVMB. Additionally, the recommended aging period and temperature for TFOT was 5 h and 178 °C, respectively. [ABSTRACT FROM AUTHOR]

This study is the first to conduct a global sensitivity analysis to identify the crucial variables that have an impact on the energy consumption of substations. The peak cooling and heating energy consumption, as well as the annual cooling and heating energy consumption of a substation in Shandong, are all simulated basing the Monte Carlo method. The simulation outputs are discussed by uncertainty analysis to obtain more accurate energy consumption thresholds. Subsequently, the treed Gaussian process and the standardized rank regression coefficient are used to perform a global sensitivity analysis of the simulation outputs. The results of the two global sensitivity analyses are practically the same, demonstrating that robustness can be ensured by simultaneously using two methods based on different theories. In addition, this study provides an efficient method for the energy-saving retrofitting of the existing substation and the energy-saving design of green substations in the future. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein. [ABSTRACT FROM AUTHOR]

Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi‐directional gated recurrent unit neural network [Bi‐GRU]). With Hong Kong inbound arrivals as a case, this study has empirically verified that deep learning techniques can improve forecasting accuracy. Furthermore, the proposed approach (i.e., SAE‐Bi‐GRU) is significantly superior to benchmark models (i.e., PCA‐Bi‐GRU with Baidu index and Google trends). [ABSTRACT FROM AUTHOR]

Code summaries are clear and concise natural language descriptions of program entities. Meaningful code summaries assist developers in better understanding. Code summarization refers to the task of generating a natural language summary from a code snippet. Most researches on code summarization focus on automatically generating summaries for methods or functions. However, in an object-oriented language such as Java, class is the basic programming unit rather than method. To fill this gap, in this paper, we investigate how to generate summaries for Java classes utilizing deep learning-based approaches. We propose a novel encoder–decoder model called ClassSum to generate functionality descriptions for Java classes and build a dataset containing 172,639 pairs from 3185 repositories hosted on Github. Since the code of class is much longer and more complicated, encoding a whole class via neural network is more challenging than encoding a method. On the other hand, the content within a class may be incomplete. To overcome this difficulty, we reduce the code of a class by only keeping its key elements, namely class signatures, method signatures and attribute names. To utilize both lexical and structural information of code, our model takes token sequence and abstract syntax tree of the reduced class content as inputs. ClassSum and five baselines (designed for method-level code summarization) are evaluated on our dataset. Experiment results show that summaries generated by ClassSum are more accurate and readable than those generated by baselines. Our dataset is available at https://github.com/classsum/ClassSum. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein. [ABSTRACT FROM AUTHOR]

Investigation of the adhesion mechanism during the aging process of the asphalt-aggregate interface from a microscopic point of view contributes to the solution of water damage of asphalt pavements. So far, some work has been done to reveal the influence of aging on the adhesion strength of the asphalt from the nanoscale factors. However, due to the lack of direct chemical analysis means to test the chemical composition of asphalt at the nanoscale, the inferences made among different scholars are somewhat controversial. The objective of this study is to reveal the evolution of adhesion characteristics of the asphalt binders during the aging process based on their chemical components. Five asphalt binders at different aging degrees were prepared via laboratory simulated aging procedure. State-of-art technologies, atomic force microscopy–based infrared spectroscopy (AFM-IR) and AFM PF-QNM (peak force quantitative nanomechanical mapping) mode, were adopted to analyze the nanoscale adhesion and chemical components of the prepared asphalt. And binder bond strength (BBS) testing was performed to interpret and verify the nanoscale mechanism of asphalt adhesion behavior from macroscale. The results of the AFM test indicated that both sulfoxide and carbonyl functional group content contribute to the adhesion force of the asphalt binders. Compared with the carbonyl group, the sulfoxide content affects the nanoscale adhesion force more significantly. Meanwhile, the results of the BBS test are found in accordance with the AFM trends. Finally, an estimation model was established to roughly estimate the nanoscale adhesion force of asphalt at varying aging conditions based on these two functional groups. [ABSTRACT FROM AUTHOR]

Code comments are valuable for program comprehension and software maintenance. However, comments can be inconsistent or out-of-date after code changes. To tackle this problem, Just-In-Time (JIT) comment updating aims to automatically update comments with code changes. Existing approaches for this task use edit sequences of source code to model code changes. Meanwhile, recent researches indicate that neural models based on abstract syntax trees (AST) can help represent source code. In this paper, we propose a new method to learn code changes by combining code edit sequences with AST edit sequences, so that the generated new comment can be more accurate. Our approach utilizes three encoders to encode code edit sequences, AST edit sequences and old comment token sequences, respectively. The outputs of the encoders are then decoded into a sequence of edit actions, which is parsed to generate a new comment. The proposed method is evaluated on a public dataset using seven metrics, and the experimental results show that our approach outperforms the baselines. Furthermore, when the new comment has a larger edit distance than the old one, our model shows better performance. [ABSTRACT FROM AUTHOR]

Decomposition methods are extensively used for processing the complex patterns of tourism demand data. Given tourism demand data's intrinsic complexity, it is critical to theoretically understand how different decomposition methods provide solutions. However, a comprehensive comparison of decomposition methods in tourism demand forecasting is still lacking. Hence, this study systematically investigates the forecasting performance of decomposition methods in tourism demand. Nine popular decomposition methods and six forecasting methods are employed, and their forecasting performance is compared. With Hong Kong visitor arrivals from eight major sources as a sample, three main conclusions are obtained from empirical results. First, all the decomposition methods generally outperform benchmark at all horizons, in both the level and directional forecasting. Second, decomposition methods can be divided into four categories based on forecasting accuracy. Finally, variational mode decomposition method is consistently superior to other eight decomposition methods and can provide the best forecasts in all cases. [ABSTRACT FROM AUTHOR]

The development of transparent electronics has advanced metal–oxide–semiconductor Thin-Film transistor (TFT) technology. In the field of flat-panel displays, as basic units, TFTs play an important role in achieving high speed, brightness, and screen contrast ratio to display information by controlling liquid crystal pixel dots. Oxide TFTs have gradually replaced silicon-based TFTs owing to their field-effect mobility, stability, and responsiveness. In the market, n-type oxide TFTs have been widely used, and their preparation methods have been gradually refined; however, p-Type oxide TFTs with the same properties are difficult to obtain. Fabricating p-Type oxide TFTs with the same performance as n-type oxide TFTs can ensure more energy-efficient complementary electronics and better transparent display applications. This paper summarizes the basic understanding of the structure and performance of the p-Type oxide TFTs, expounding the research progress and challenges of oxide transistors. The microstructures of the three types of p-Type oxides and significant efforts to improve the performance of oxide TFTs are highlighted. Finally, the latest progress and prospects of oxide TFTs based on p-Type oxide semiconductors and other p-Type semiconductor electronic devices are discussed. [ABSTRACT FROM AUTHOR]

Optimizing building performance and economic benefits through feedback in building design is a hot topic in current academic research. However, few studies on prefabricated buildings have been undertaken in this field. Meanwhile, the methodology used for achieving optimized solutions is still poor. In this paper, genetic algorithms and correlation analysis are employed and two parametric design methods—i.e., the floor plan generation method and the component selection method—are proposed for the modularity of the prefabricated buildings. Taking a typical high-rise building in Tianjin as an example, correlation analyses are performed on the basis of the two proposed methods to enhance the depth of the optimized finding approach. The outcome of this research demonstrates the feasibility of the proposed numerical approach, which can produce the optimized floor plan and construction set under the local conditions. This also reveals that the shape coefficient and window-to-wall ratio are strongly correlated with the energy performance of a building, which can help architects to pursue optimized design solutions in the schematic design process. [ABSTRACT FROM AUTHOR]

Banxia Baizhu Tianma decoction (BBTD), a six-herb Chinese medicine formula first described approximately 1732 AD, is commonly prescribed for Hypertension with Phlegm-dampness Stagnation (HPDS) as an adjuvant therapy in China. Obesity is an important risk factor for the increasing prevalence of hypertension year by year in China. In Traditional Chinese medicine, obesity is often differentiated as the syndrome of excessive phlegm-dampness.Vascular endothelial cell injury plays an important role in the development and occurrence of HPDS. In this study, the protective effects of 18 batches of BBTD samples from different origins on HUVEC cells were evaluated, including antioxidant and anti-inflammatory activities. Ultrahigh performance liquid chromatography (UPLC) was used to establish fingerprints, and combined with pharmacodynamic indexes, the protective components of BBTD on endothelial cells were analyzed. Antioxidant and anti-inflammatory activities were evaluated by ROS and Hs-CRP models, respectively. Hierarchical cluster analysis (HCA) and Bivariate correlation analysis (BCA) were used to investigate the potential correlation between chemical components and endothelial cell protection. The results indicated that BBTD could reduce ROS and hs-CRP levels in HUVEC cells, and the pharmacological activities in 18 batches of BBTD samples were significantly different. The results of BCA indicated that Gastrodin, Liquiritin, Hesperidin, Isoliquiritin, Hesperetin, and Isoliquiritigenin might be the active constituents to activate ROS and suppress hs-CRP as determined by spectrum-effect relationships. The antioxidant and anti-inflammatory activities of the 6 components at different concentration were verified, and the results showed that all of them had good antioxidant and anti-inflammatory activities in a concentration-dependent manner. This study showed that activity determination and spectral correlation can be used to search for active substances in Chinese medicine formula and provide data support for quality control of Traditional Chinese medicine (TCM). [ABSTRACT FROM AUTHOR]

If HT ht , additional tests should be conducted. These factors include the asphalt amount, physical properties of asphalt, the gradation and characteristics of aggregates, and asphalt-aggregate interface characteristics ([3]; [4]). It can be seen that similar to 70# base asphalt, the rutting resistance at high temperatures also can be improved using the elaborated mixing process proposed in the original paper for SBS-modified asphalt and hard-grade asphalt. [Extracted from the article]

Background: This study aimed to explore the effect of nesfatin-1 on cobalt chloride (CoCl2)-induced hypoxic injury in cardiomyocyte H9c2 cells. Methods: H9c2 cardiomyocytes were induced by different concentrations of CoCl2 to mimic the hypoxia condition. Cell viability was detected by MTT assay. Cell apoptosis was detected by TUNEL staining and flow cytometry. ROS production was detected using the fluorescence probe DCFH-DA. The mitochondrial membrane potential (MMP) was detected using the TMRE method. The levels of released lactate dehydrogenase (LDH), malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT) were detected using the commercial kits. The protein levels of MAPK signaling members (p-JNK1/2, p-ERK1/2, and p-p38) and Notch1 signaling members (Notch1, Hes 1, and Jagged 1) were detected by Western blot. Results: CoCl2 significantly promoted cell apoptosis, increased LDH leakage, MDA concentration, and decreased cell viability, SOD activity, GSH production, and CAT activity. CoCl2-induced hypoxic injury in H9c2 cells was partially restored by nesfatin-1 treatment. Moreover, nesfatin-1 treatment attenuated CoCl2-induced increase in ROS production and mitochondrial dysfunction, decreased mitochondrial membrane potential, Bax/Bcl-2 imbalance, as well as c-caspase-9 and c-caspase-3 levels. Moreover, nesfatin-1 treatment inhibited the activation of MAPK and Notch1 signaling pathways. Conclusions: Nesfatin-1 could effectively protect H9c2 cells against CoCl2-induced hypoxic injury by blocking MAPK and Notch1 signaling pathways, suggesting that nesfatin-1 might be a promising therapeutic agent for hypoxic cardiac injury. [ABSTRACT FROM AUTHOR]

Community detection is of great importance for online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing the way researchers analyze those networks. For instance, real-world networks, such as Facebook, LinkedIn and Twitter, are inherently growing rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the static networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the dynamic networks. We propose DynaMo, a novel modularity-based dynamic community detection algorithm, aiming to detect communities of dynamic networks as effective as repeatedly applying static algorithms but in a more efficient way. DynaMo is an adaptive and incremental algorithm, which is designed for incrementally maximizing the modularity gain while updating the community structure of dynamic networks. In the experimental evaluation, a comprehensive comparison has been made among DynaMo, Louvain (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real-world networks and 10,000 synthetic networks. Our results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of the effectiveness, and is 2 to 5 times (by average) faster than Louvain algorithm. [ABSTRACT FROM AUTHOR]

SUMMARY: In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Batch crystallization in acidic aqueous solution of cephalexin was conducted by reactive crystallization with or without seeding. Supersaturation was generated by mixing ammonia and acidic aqueous solution of cephalexin and controlled by solution feeding rate and seeding conditions. UV and Morphologi G3 were used to measure supersaturation and aspect ratio. Experimental results demonstrated that burst nucleation occurred and the products were needle-like at high supersaturation; meanwhile, the products were plate-like and had high aspect ratio at low supersaturation. Analysis of the measured supersaturation profiles and corresponding aspect ratio explained the mechanisms governing the aspect ratio. The optimized operating parameters were also proposed (seeding supersaturation is equal to 1.3, seed mass ratio 8% and feeding rate 368 μL/min). [ABSTRACT FROM AUTHOR]

Human detection has received great attention during the past few decades, which is yet still a challenging problem. In this paper, we focus on the problem of 3-D human detection, i.e., finding the human bodies and determining their 3-D coordinates in complex 3-D space using depth data only. Since the traditional sliding-window-based approaches for target localization are time-consuming and the recent deep-learning-based object detectors generate too many region proposals, we propose to utilize the candidate head-top locating stage to efficiently and quickly find the plausible head-top locations. In the second stage, we propose a Depth map, Multiorder depth template, and Height difference map representation encoding three channels of information for each candidate region to utilize the neural network pretrained on large-scale well-annotated datasets to classify the candidate regions. We evaluate our method on four publicly available challenging datasets. Extensive experimental results demonstrate that the proposed method is superior to the state-of-the-art methods while achieving real-time performance. [ABSTRACT FROM AUTHOR]

Veratryl alcohol (VA) is a product from the biodegradation of lignocellulosic biomass. The objective of this study was to explore the possibility whether VA could be used as the fuel of the microbial fuel cell (MFC) to generate power. Two types of MFCs, a two-chamber MFC and a single-chamber air-cathode MFC, were set up for experiments. In the two-chamber MFC, average maximum current outputs higher than 700 μA were obtained using various mixtures of glucose and VA as the fuel. The highest power density of 35.17 W m-3 was achieved using the mixture of 1000 mg L-1 glucose and 50 mg L-1 VA as the fuel. With 500 mg L-1 VA as the fuel in the MFC, we obtained an average maximum current output of 181 μA. In the single-chamber MFC, the maximum current output reached up to 178 μA with 500 mg L-1 VA in the fed-batch mode and the maximum CE reached 23.77% with 100 mg L-1 VA. At the end of all operation cycles of the MFCs, the glucose and VA were undetectable in the solution, and the removal efficiencies of COD were between 75% and 88%. The denaturing gradient gel electrophoresis profiles of 16S rRNA gene indicated that the dominant species on the anode biofilm did not change significantly with the different substrates, but the abundance of some species increased greatly. The scanning electron micrographs showed that the most abundant bacteria on the electrode were bacilli. The dominant species belonged to bacteroidetes and proteobacterium. [ABSTRACT FROM AUTHOR]