Reentrant phase transitions of higher-dimensional AdS black holes in dRGT massive gravity

We study the $P-V$ criticality and phase transition in the extended phase space of anti-de Sitter (AdS) black holes in higher-dimensional de Rham, Gabadadze and Tolley (dRGT) massive gravity, treating the cosmological constant as pressure and the corresponding conjugate quantity is interpreted as thermodynamic volume. Besides the usual small/large black hole phase transitions, the interesting thermodynamic phenomena of reentrant phase transitions (RPTs) are observed for black holes in all $d\geq6$-dimensional spacetime when the coupling coefficients $c_i m^2$ of massive potential satisfy some certain conditions.Comment: 14 pages, several references are added, v2: published in EPJ

Solution Routes to Metal Chalcogenide and Zinc Oxide Thin Films for Device Applications

Semiconductor thin films and their fabrication methods have been intensively investigated in order to fulfill a wide spectrum of applications in electronic devices, optical devices, and photoelectronic devices. Solution deposition of semiconductor thin films draws increasing attention since it offers low cost, high-throughput production, and better compatibility with flexible or polymeric substrates compared to the vacuum deposition. This dissertation mainly focuses on the solution routes for depositing two groups of seminconductor thin films: kesterite copper zinc tin sulfide/selenide/sulfoselenide (Cu2ZnSn(S1-x Sex)4 (0≤x≤1) (CZTSSe) and zinc oxides (ZnO). The corresponding thin film devices based on the solution routes are also presented

Motion-state Alignment for Video Semantic Segmentation

In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentation to keep both motion and state consistency. In the framework, we first construct a motion alignment branch armed with an efficient decoupled transformer to capture dynamic semantics, guaranteeing region-level temporal consistency. Then, a state alignment branch composed of a stage transformer is designed to enrich feature spaces for the current frame to extract static semantics and achieve pixel-level state consistency. Next, by a semantic assignment mechanism, the region descriptor of each semantic category is gained from dynamic semantics and linked with pixel descriptors from static semantics. Benefiting from the alignment of these two kinds of effective information, the proposed method picks up dynamic and static semantics in a targeted way, so that video semantic regions are consistently segmented to obtain precise locations with low computational complexity. Extensive experiments on Cityscapes and CamVid datasets show that the proposed approach outperforms state-of-the-art methods and validates the effectiveness of the motion-state alignment framework.Comment: Accepted by CVPR Workshops 202

The Development and Application of Crop Evaluation System Based on GRA

Ever since it was proposed, grey system theory has attracted the attention of scientific researchers and scholars. And it also has been widely used in many fields and solved a large number of practical problems in production, life, and scientific research. With the development and popularization of computer science and network technology, this traditional mathematical model can be applied more simply and efficiently to solve practical problems. Firstly, this paper, to implement steps of grey relational analysis, has made the exclusive analysis and has made the simple introduction to grey relational analysis characteristics. Then, based on grey relational theory and ASP.NET technology, the crop evaluation system is developed. Lastly, by using Excel and the crop evaluation system, the paper carries out a comprehensive evaluation about eight features of Fuji apple, which is from nine different producing areas, respectively. The experiment results show that the crop evaluation system is effective and could greatly improve the work efficiency of the researcher and expand the application scope

Ammonia and Carbon Dioxide Emissions vs. Feeding and Defecation Activities of Laying Hens

This study characterizes dynamic ammonia (NH3) and carbon dioxide (CO2) emissions associated with feeding and defecation activities of laying hens. Manure handling scheme used was reflective of commercial manure-belt house operation. Four dynamic emission chambers and measurement system was developed, featuring continuous measurement of the following variables for each chamber: (a) NH3 concentrations of inlet and outlet air, (b) air temperature and relative humidity, (c) airflow rate, (d) feeder weight and thus feeding activity, and (e) manure pan weight and thus defecation activity. Daily feed consumption of the hens averaged 103 g/hen-d and fresh manure production averaged 125 g/hen-d. Ammonia emission rate ranged from 1.26 mg/hen-hr on the first day of manure accumulation to 9.26 mg/hen-hr after 7 d of manure accumulation. CO2 emission rate averaged 3.41 and 2.47 g/hen-hr during light and dark hours of the day, respectively. Dynamic NH3 emissions tend to be inversely related to defecation events as manure accumulates. Results from this study will contribute to the development and/or validation of process-based farm emission model for predicting NH3 emissions from laying-hen houses. The dynamic nature of NH3 emissions vs. defecation may also provide insight concerning application timing of manure treatment agents to mitigate NH3 emissions from laying-hen houses

Selective unresponsiveness to the inhibition of p38 MAPK activation by cAMP helps L929 fibroblastoma cells escape TNF-α-induced cell death

<p>Abstract</p> <p>Background</p> <p>The cyclic AMP (cAMP) signaling pathway has been reported to either promote or suppress cell death, in a cell context-dependent manner. Our previous study has shown that the induction of dynein light chain (DLC) by cAMP response element-binding protein (CREB) is required for cAMP-mediated inhibition of mitogen-activated protein kinase (MAPK) p38 activation in fibroblasts, which leads to suppression of NF-κB activity and promotion of tumor necrosis factor-α (TNF-α)-induced cell death. However, it remains unknown whether this regulation is also applicable to fibroblastoma cells.</p> <p>Methods</p> <p>Intracellular cAMP was determined in L929 fibroblastoma cells after treatment of the cells with various cAMP elevation agents. Effects of cAMP in the presence or absence of the RNA synthesis inhibitor actinomycin D or small interfering RNAs (siRNAs) against CREB on TNF-α-induced cell death in L929 cells were measured by propidium iodide (PI) staining and subsequent flow cytomety. The activation of p38 and c-Jun N-terminal protein kinase (JNK), another member of MAPK superfamily, was analyzed by immunoblotting. JNK selective inhibitor D-JNKi1 and p38 selective inhibitor SB203580 were included to examine the roles of JNK and p38 in this process. The expression of DLC or other mediators of cAMP was analyzed by immunoblotting. After ectopic expression of DLC with a transfection marker GFP, effects of cAMP on TNF-α-induced cell death in GFP+ cells were measured by PI staining and subsequent flow cytomety.</p> <p>Results</p> <p>Elevation of cAMP suppressed TNF-α-induced necrotic cell death in L929 fibroblastoma cells via CREB-mediated transcription. The pro-survival role of cAMP was associated with selective unresponsiveness of L929 cells to the inhibition of p38 activation by cAMP, even though cAMP significantly inhibited the activation of JNK under the same conditions. Further exploration revealed that the induction of DLC, the major mediator of p38 inhibition by cAMP, was impaired in L929 cells. Enforced inhibition of p38 activation by using p38 specific inhibitor or ectopic expression of DLC reversed the protection of L929 cells by cAMP from TNF-α-induced cell death.</p> <p>Conclusion</p> <p>These data suggest that the lack of a pro-apoptotic pathway in tumor cells leads to a net survival effect of cAMP.</p

Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management

People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses and label clusters quickly. In addition, to process extremely noisy and often rather short user-generated messages, we enrich tweet representations using preceding context tweets and reply tweets in building event recognition classifiers. The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained using more than ten thousand annotated tweets created in over 50 person-hours.Comment: In Proceedings of the AAAI 2020 (AI for Social Impact Track). Link: https://aaai.org/ojs/index.php/AAAI/article/view/539

Research Progress in Anaerobic Digestion of High Moisture

High moisture organic waste constitutes a large fraction of municipal solid waste and caused a nuisance. Anaerobic digestion of this high degradable fraction has been developed during the past 20 years. Parameters such as characteristic of substrates, temperature, organic loading rate and hydraulic retention time were studied. The most important conversion of intermediate of volatile fatty acid was observed as a indicator of digestion efficiency. One stage and two stage system are based on the stage separated into acidogenic phase and methnogenis phase. Two stage digestion of this kind of wastes were proved a better efficiency than single stage digestion. Batch system and continuous system are conducted in single stage and two-stage system. One stage system are split between wet system(Total solid less than 15%) and dry system( total solid higher than 15%) according to the characteristics of feedstock. Two-stage solid bed system are observed more and more popular in the digestion of solid state VFW and food waste experimental studies, however the large majority of industrial application use single stage systems. Two stage digestion of HMOW will be applied to industrial scale due to its larger resistance to high loading rate, high and stable gas production

A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions

Deep learning techniques have been widely used in computed tomography (CT) but require large data sets to train networks. Moreover, data sharing among multiple institutions is limited due to data privacy constraints, which hinders the development of high-performance DL-based CT imaging models from multi-institutional collaborations. Federated learning (FL) strategy is an alternative way to train the models without centralizing data from multi-institutions. In this work, we propose a novel peer-to-peer federated continual learning strategy to improve low-dose CT imaging performance from multiple institutions. The newly proposed method is called peer-to-peer continual FL with intermediate controllers, i.e., icP2P-FL. Specifically, different from the conventional FL model, the proposed icP2P-FL does not require a central server that coordinates training information for a global model. In the proposed icP2P-FL method, the peer-to-peer federated continual learning is introduced wherein the DL-based model is continually trained one client after another via model transferring and inter institutional parameter sharing due to the common characteristics of CT data among the clients. Furthermore, an intermediate controller is developed to make the overall training more flexible. Numerous experiments were conducted on the AAPM low-dose CT Grand Challenge dataset and local datasets, and the experimental results showed that the proposed icP2P-FL method outperforms the other comparative methods both qualitatively and quantitatively, and reaches an accuracy similar to a model trained with pooling data from all the institutions