Construction of Highly Stable Metal-Organic Frameworks with Multiple Functionalities

Metal-organic frameworks (MOFs) are a class of newly emerged crystalline porous materials consisting of metal ions or clusters and organic linkers. Through judicious choice of inorganic joints and organic struts, the structure, porosity and functionality of MOFs can be tuned. However, the lack of high stability of most of the reported MOFs as well as limited methods to introduce multiple functionalities into the framework hinders the exploration of MOFs towards a wide variety of potential applications. The main goal of this research is to develop synthetic strategies to construct MOFs with high stability and multiple functionalities. Firstly, a brief introduction of MOFs was provided, focusing on strategies to increase their stability and introduce functionalities. Secondly, a facile one-pot synthetic strategy was developed to introduce porphyrin into highly stable UiO-66 homogeneously. The crystal structure, morphology, and ultrahigh chemical stability of UiO-66 were well maintained in the functionalized MOFs. In addition, the amount of integrated porphyrin can be gradually tuned. Thirdly, a general in situ secondary ligand incorporation (ISLI) strategy was investigated to synthesize multivariate UiO series of MOFs. Both experimental and computational studies were carried out to understand the chemistry behind this strategy. Fourthly, ISLI strategy was further applied in highly stable Zr-MOFs constructed from multitopic ligands to incorporate multiple functionalities. Fifthly, a porphyrin and pyrene-based mixed-ligand MOF with high stability and novel topology was synthesized. This MOF provides an ideal platform for further functionalization and exploration of new structures. In summary, different strategies were investigated to construct highly stable MOFs with the incorporation of multiple functionalities. These studies provide useful tools to explore stable MOFs with desired multifunctionality for potential applications

Text processing using neural networks

Natural language processing is a key technology in the field of artificial intelligence. It involves the two basic tasks of natural language understanding and natural language generation. The primary core of solving the above tasks is to obtain text semantics. Text semantic analysis enables computers to simulate humans to understand the deep semantics of natural language and identify the true meaning contained in information by building a model. Obtaining the true semantics of text helps to improve the processing effect of various natural language processing downstream tasks, such as machine translation, question answering systems, and chatbots. Natural language text is composed of words, sentences and paragraphs (in that order). Word-level semantic analysis is concerned with the sense of words, the quality of which directly affects the quality of subsequent text semantics at each level. Sentences are the simplest sequence of semantic units, and sentence-level semantics analysis focuses on the semantics expressed by the entire sentence. Paragraph semantic analysis achieves the purpose of understanding paragraph semantics. Currently, while the performance of semantic analysis models based on Deep Neural Network has made significant progress, many shortcomings remain. This thesis proposes the Deep Neural Network-based model for sentence semantic understanding, word sense understanding and text sequence generation from the perspective of different research tasks to address the difficulties in text semantic analysis. The research contents and contributions are summarized as follows: First, the mainstream use of recurrent neural networks cannot directly model the latent structural information of sentences. To better determine the sense of ambiguous words, this thesis proposes a model that uses a two-layer bi-directional long short-term memory neural network and attention mechanism. Second, static word embedding models cannot manage polysemy. Contextual word embedding models can do so, however, their performance is limited in application scenarios with high real-time requirements. Accordingly, this thesis proposes using a word sense induction task to construct word sense embeddings for polysemous words. Third, the current mainstream encoder-decoder model based on the attention mechanism does not explicitly perform a preliminary screening of the information in the source text before summary generation. This results in the input to the decoder containing a large amount of information irrelevant to summary generation as well as exposure bias and out-of-vocabulary words in the generation of sequences. To address this problem, this thesis proposes an abstractive text summarization model based on a hierarchical attention mechanism and multi-objective reinforcement learning. In summary, this thesis conducts in-depth research on semantic analysis, and proposes solutions to problems in word sense disambiguation, word sense embeddings, and abstractive text summarization tasks. The feasibility and validity were verified through extensive experiments on their respective corresponding publicly-available standard datasets, and also provide support for other related research in the field of natural language processing.Natural language processing is a key technology in the field of artificial intelligence. It involves the two basic tasks of natural language understanding and natural language generation. The primary core of solving the above tasks is to obtain text semantics. Text semantic analysis enables computers to simulate humans to understand the deep semantics of natural language and identify the true meaning contained in information by building a model. Obtaining the true semantics of text helps to improve the processing effect of various natural language processing downstream tasks, such as machine translation, question answering systems, and chatbots. Natural language text is composed of words, sentences and paragraphs (in that order). Word-level semantic analysis is concerned with the sense of words, the quality of which directly affects the quality of subsequent text semantics at each level. Sentences are the simplest sequence of semantic units, and sentence-level semantics analysis focuses on the semantics expressed by the entire sentence. Paragraph semantic analysis achieves the purpose of understanding paragraph semantics. Currently, while the performance of semantic analysis models based on Deep Neural Network has made significant progress, many shortcomings remain. This thesis proposes the Deep Neural Network-based model for sentence semantic understanding, word sense understanding and text sequence generation from the perspective of different research tasks to address the difficulties in text semantic analysis. The research contents and contributions are summarized as follows: First, the mainstream use of recurrent neural networks cannot directly model the latent structural information of sentences. To better determine the sense of ambiguous words, this thesis proposes a model that uses a two-layer bi-directional long short-term memory neural network and attention mechanism. Second, static word embedding models cannot manage polysemy. Contextual word embedding models can do so, however, their performance is limited in application scenarios with high real-time requirements. Accordingly, this thesis proposes using a word sense induction task to construct word sense embeddings for polysemous words. Third, the current mainstream encoder-decoder model based on the attention mechanism does not explicitly perform a preliminary screening of the information in the source text before summary generation. This results in the input to the decoder containing a large amount of information irrelevant to summary generation as well as exposure bias and out-of-vocabulary words in the generation of sequences. To address this problem, this thesis proposes an abstractive text summarization model based on a hierarchical attention mechanism and multi-objective reinforcement learning. In summary, this thesis conducts in-depth research on semantic analysis, and proposes solutions to problems in word sense disambiguation, word sense embeddings, and abstractive text summarization tasks. The feasibility and validity were verified through extensive experiments on their respective corresponding publicly-available standard datasets, and also provide support for other related research in the field of natural language processing.460 - Katedra informatikyvyhově

Propozycja standardu ekologicznej kompensacji dla obszarowych zanieczyszczeń z rolnictwa

Non-point source water pollution mainly comes from farmland chemical fertilizers which has become an obstacle of agricultural sustainability and ecological health. As a public policy tool for assessing global ecological crisis and environmental pollution, ecological compensation is important for regional agricultural sustainability. Ecological compensation that farmers receive from governments is based on their reduction of fertilizer application at optimal ecological and economic levels. In this study we estimated the ecological compensation standards for nitrogen non-point pollution in Yixng city with contingent valuation method and cost-benefit method.  Results showed that the range of theoretical values of ecological compensation of nitrogen in Yixing City depended upon its optimal ecological and economic nitrogen application levels. The willingness of farmers to accept the compensation was positively correlated with their farming experience and education. There were about half of farmers willing to accept the compensation. Based on the present study, we found Yixing’s ecological compensation standard for controlling nitrogen non-point pollution was 620.0 yuan/hm2 at the current economic development level.Obszarowe zanieczyszczeń wód z rolnictwa pochodzą ze stosowania nawozów sztucznych, stanowiących przeszkodę na drodze do osiągnięcia rolniczej zrównoważoności i równowagi ekologicznej. W tym kontekście ekologiczna kompensacja, stanowiąca narzędzie polityczne do oceny kryzysu ekologicznego i ogólnego poziomu zanieczyszczenia środowiska, okazuje się także ważna w wymiarze lokalnej zrównoważoności rolniczej. Wysokość świadczeń, które rolniczy dostają od władz, jest uwarunkowana poziomem redukcji stosowania nawozów, którego celem jest osiągnięcie poziomu optymalnego zarówno zer strony ekologicznej, jak i ekonomicznej. W tym artykule, przy pomocy  Metoda wyceny warunkowej i metody kosztów i korzyści, ustaliliśmy standardy ekologicznej kompensacji dla miasta Yixng. Otrzymane rezultaty pozwalają na stwierdzenie, że zakres teoretycznych wartości ekologicznej kompensacji dla azotu w Yixing zależy od ustalenia optymalnych ekologicznych i ekonomicznych pozimów stosowania azotu. Zainteresowanie rolników otrzymaniem odszkodowania okazało się być pozytywnie skorelowane z ich doświadczeniem rolniczym i poziomem wykształcenia. Chęć jego otrzymania zgłosiła połowa z nich. Ustaliliśmy ponadto, że standard ekologicznej kompensacji dla Yixing odnoszący się kontrolowania obszarowych zanieczyszczeń związanych z nawozami azotowymi wynosi 620.0 yuan/hm2 , przy założeniu obecnego poziomu rozwoju ekonomicznego

Recent progress in the synthesis of metal–organic frameworks

Metal–organic frameworks (MOFs) have attracted considerable attention for various applications due to their tunable structure, porosity and functionality. In general, MOFs have been synthesized from isolated metal ions and organic linkers under hydrothermal or solvothermal conditions via one-spot reactions. The emerging precursor approach and kinetically tuned dimensional augmentation strategy add more diversity to this field. In addition, to speed up the crystallization process and create uniform crystals with reduced size, many alternative synthesis routes have been explored. Recent advances in microwave-assisted synthesis and electrochemical synthesis are presented in this review. In recent years, post-synthetic approaches have been shown to be powerful tools to synthesize MOFs with modified functionality, which cannot be attained via de novo synthesis. In this review, some current accomplishments of post-synthetic modification (PSM) based on covalent transformations and coordinative interactions as well as post-synthetic exchange (PSE) in robust MOFs are provided

Multi-channel all-optical signal processing based on parametric effects

Two different experiments that use parametric effects for the processing of multiple signals in a single fiber are reviewed. The first experiment uses optical phase conjugation to mitigate the effects of nonlinearity in transmission, whereas the second uses multiple phase-sensitive amplifiers to regenerate six different channels

Open-Vocabulary Video Anomaly Detection

Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. However, current approaches are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. However, such a setting focuses on predicting frame anomaly scores, having no ability to recognize the specific categories of anomalies, despite the fact that this ability is essential for building more informed video surveillance systems. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies. To this end, we propose a model that decouples OVVAD into two mutually complementary tasks -- class-agnostic detection and class-specific classification -- and jointly optimizes both tasks. Particularly, we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task, and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task. These semantic knowledge and synthesis anomalies substantially extend our model's capability in detecting and categorizing a variety of seen and unseen anomalies. Extensive experiments on three widely-used benchmarks demonstrate our model achieves state-of-the-art performance on OVVAD task.Comment: Submitte