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This paper embarks on an exploration into the Large Language Model (LLM) datasets, which play a crucial role in the remarkable advancements of LLMs. The datasets serve as the foundational infrastructure analogous to a root system that sustains and nurtures the development of LLMs. Consequently, examination of these datasets emerges as a critical topic in research. In order to address the current lack of a comprehensive overview and thorough analysis of LLM datasets, and to gain insights into their current status and future trends, this survey consolidates and categorizes the fundamental aspects of LLM datasets from five perspectives: (1) Pre-training Corpora; (2) Instruction Fine-tuning Datasets; (3) Preference Datasets; (4) Evaluation Datasets; (5) Traditional Natural Language Processing (NLP) Datasets. The survey sheds light on the prevailing challenges and points out potential avenues for future investigation. Additionally, a comprehensive review of the existing available dataset resources is also provided, including statistics from 444 datasets, covering 8 language categories and spanning 32 domains. Information from 20 dimensions is incorporated into the dataset statistics. The total data size surveyed surpasses 774.5 TB for pre-training corpora and 700M instances for other datasets. We aim to present the entire landscape of LLM text datasets, serving as a comprehensive reference for researchers in this field and contributing to future studies. Related resources are available at: https://github.com/lmmlzn/Awesome-LLMs-Datasets., Comment: 181 pages, 21 figures

Lung cancer is a devastating disease with the highest mortality rate among cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, which accounts for 87% of diagnoses, require radiation therapy. Rapid treatment initiation significantly increases the patient's survival rate and reduces the mortality rate. Accurate tumor segmentation is a critical step in the diagnosis and treatment of NSCLC. Manual segmentation is time and labor-consuming and causes delays in treatment initiation. Although many lung nodule detection methods, including deep learning-based models, have been proposed, there is still a long-standing problem of high false positives (FPs) with most of these methods. Here, we developed an electronic health record (EHR) guided lung tumor auto-segmentation called EXACT-Net (EHR-enhanced eXACtitude in Tumor segmentation), where the extracted information from EHRs using a pre-trained large language model (LLM), was used to remove the FPs and keep the TP nodules only. The auto-segmentation model was trained on NSCLC patients' computed tomography (CT), and the pre-trained LLM was used with the zero-shot learning approach. Our approach resulted in a 250% boost in successful nodule detection using the data from ten NSCLC patients treated in our institution.

Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion. Most public datasets currently contain only PDF documents and lack realistic documents. Models trained on these datasets may not generalize well to real-world scenarios. Therefore, this paper introduces a large and diverse document layout analysis dataset called $M^{6}Doc$. The $M^6$ designation represents six properties: (1) Multi-Format (including scanned, photographed, and PDF documents); (2) Multi-Type (such as scientific articles, textbooks, books, test papers, magazines, newspapers, and notes); (3) Multi-Layout (rectangular, Manhattan, non-Manhattan, and multi-column Manhattan); (4) Multi-Language (Chinese and English); (5) Multi-Annotation Category (74 types of annotation labels with 237,116 annotation instances in 9,080 manually annotated pages); and (6) Modern documents. Additionally, we propose a transformer-based document layout analysis method called TransDLANet, which leverages an adaptive element matching mechanism that enables query embedding to better match ground truth to improve recall, and constructs a segmentation branch for more precise document image instance segmentation. We conduct a comprehensive evaluation of $M^{6}Doc$ with various layout analysis methods and demonstrate its effectiveness. TransDLANet achieves state-of-the-art performance on $M^{6}Doc$ with 64.5% mAP. The $M^{6}Doc$ dataset will be available at https://github.com/HCIILAB/M6Doc., Comment: Accepted by CVPR 2023

In recent years, the optical character recognition (OCR) field has been proliferating with plentiful cutting-edge approaches for a wide spectrum of tasks. However, these approaches are task-specifically designed with divergent paradigms, architectures, and training strategies, which significantly increases the complexity of research and maintenance and hinders the fast deployment in applications. To this end, we propose UPOCR, a simple-yet-effective generalist model for Unified Pixel-level OCR interface. Specifically, the UPOCR unifies the paradigm of diverse OCR tasks as image-to-image transformation and the architecture as a vision Transformer (ViT)-based encoder-decoder. Learnable task prompts are introduced to push the general feature representations extracted by the encoder toward task-specific spaces, endowing the decoder with task awareness. Moreover, the model training is uniformly aimed at minimizing the discrepancy between the generated and ground-truth images regardless of the inhomogeneity among tasks. Experiments are conducted on three pixel-level OCR tasks including text removal, text segmentation, and tampered text detection. Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models. Code will be publicly available.

Recently, there has been a growing interest in research concerning document image analysis and recognition in photographic scenarios. However, the lack of labeled datasets for this emerging challenge poses a significant obstacle, as manual annotation can be time-consuming and impractical. To tackle this issue, we present DocAligner, a novel method that streamlines the manual annotation process to a simple step of taking pictures. DocAligner achieves this by establishing dense correspondence between photographic document images and their clean counterparts. It enables the automatic transfer of existing annotations in clean document images to photographic ones and helps to automatically acquire labels that are unavailable through manual labeling. Considering the distinctive characteristics of document images, DocAligner incorporates several innovative features. First, we propose a non-rigid pre-alignment technique based on the document's edges, which effectively eliminates interference caused by significant global shifts and repetitive patterns present in document images. Second, to handle large shifts and ensure high accuracy, we introduce a hierarchical aligning approach that combines global and local correlation layers. Furthermore, considering the importance of fine-grained elements in document images, we present a details recurrent refinement module to enhance the output in a high-resolution space. To train DocAligner, we construct a synthetic dataset and introduce a self-supervised learning approach to enhance its robustness for real-world data. Through extensive experiments, we demonstrate the effectiveness of DocAligner and the acquired dataset. Datasets and codes will be publicly available.

Metastatic breast cancer (MBC) is the leading cause of breast cancer (BC) related morbidity and mortality. Treatment resistance and tumor evolution and heterogeneity are major challenges of managing MBC. This thesis set out to investigate MBC evolution and heterogeneity through the lens of multi-omics to uncover drivers and mechanisms of endocrine resistance and guide precision treatment of BC bone metastasis (BoM). We previously identified the overexpression of FGFR4 in endocrine resistant samples. Further in this thesis, we demonstrated that high FGFR4 predicts poorer survival of patients with ER+ BC and ER suppresses FGFR4 expression. However, FGFR4 cannot drive endocrine resistance in ER+ BC cell lines in vitro. FGFR4 inhibitor had a minimal effect on HER2 non-amplified cell growth but a stronger inhibition of HER2 amplified cell growth and FGFR4 showed the highest expression in HER2-enriched BC, suggesting HER2 expression may be required for FGFR4 function in BC which should be further explored. To verify FGFR4 overexpression post endocrine therapy in vivo and investigate tumor microenvironment (TME) contribution to endocrine resistance, an ER+ SSM3 syngeneic murine homograft model was utilized. Results showed an increase of myeloid cell infiltration post endocrine therapy. To investigate the potential of multi-omics guided precision MBC treatment, we performed DNA/RNA/single cell RNA (scRNA) analyses of bilateral BoM, with matched primary invasive lobular breast carcinoma and patient derived organoids (PDO). BoM lost estrogen receptor expression, gained BRCA1 (D1834H) mutation, and upregulated multiple targetable cancer hallmark pathways. scRNA analysis uncovered a complicated microenvironment of BoM consisting of multiple epithelial cell subclones communicating extensively with TME. PDO faithfully retained features of originating BoM and were responsive to targeting driver mutations in vitro and in vivo. In summary, I performed a comprehensive analysis of MBC

Metastatic breast cancer (MBC) is the leading cause of breast cancer (BC) related morbidity and mortality. Treatment resistance and tumor evolution and heterogeneity are major challenges of managing MBC. This thesis set out to investigate MBC evolution and heterogeneity through the lens of multi-omics to uncover drivers and mechanisms of endocrine resistance and guide precision treatment of BC bone metastasis (BoM). We previously identified the overexpression of FGFR4 in endocrine resistant samples. Further in this thesis, we demonstrated that high FGFR4 predicts poorer survival of patients with ER+ BC and ER suppresses FGFR4 expression. However, FGFR4 cannot drive endocrine resistance in ER+ BC cell lines in vitro. FGFR4 inhibitor had a minimal effect on HER2 non-amplified cell growth but a stronger inhibition of HER2 amplified cell growth and FGFR4 showed the highest expression in HER2-enriched BC, suggesting HER2 expression may be required for FGFR4 function in BC which should be further explored. To verify FGFR4 overexpression post endocrine therapy in vivo and investigate tumor microenvironment (TME) contribution to endocrine resistance, an ER+ SSM3 syngeneic murine homograft model was utilized. Results showed an increase of myeloid cell infiltration post endocrine therapy. To investigate the potential of multi-omics guided precision MBC treatment, we performed DNA/RNA/single cell RNA (scRNA) analyses of bilateral BoM, with matched primary invasive lobular breast carcinoma and patient derived organoids (PDO). BoM lost estrogen receptor expression, gained BRCA1 (D1834H) mutation, and upregulated multiple targetable cancer hallmark pathways. scRNA analysis uncovered a complicated microenvironment of BoM consisting of multiple epithelial cell subclones communicating extensively with TME. PDO faithfully retained features of originating BoM and were responsive to targeting driver mutations in vitro and in vivo. In summary, I performed a comprehensive analysis of MBC

Camera-captured document images usually suffer from perspective and geometric deformations. It is of great value to rectify them when considering poor visual aesthetics and the deteriorated performance of OCR systems. Recent learning-based methods intensively focus on the accurately cropped document image. However, this might not be sufficient for overcoming practical challenges, including document images either with large marginal regions or without margins. Due to this impracticality, users struggle to crop documents precisely when they encounter large marginal regions. Simultaneously, dewarping images without margins is still an insurmountable problem. To the best of our knowledge, there is still no complete and effective pipeline for rectifying document images in the wild. To address this issue, we propose a novel approach called Marior (Margin Removal and \Iterative Content Rectification). Marior follows a progressive strategy to iteratively improve the dewarping quality and readability in a coarse-to-fine manner. Specifically, we divide the pipeline into two modules: margin removal module (MRM) and iterative content rectification module (ICRM). First, we predict the segmentation mask of the input image to remove the margin, thereby obtaining a preliminary result. Then we refine the image further by producing dense displacement flows to achieve content-aware rectification. We determine the number of refinement iterations adaptively. Experiments demonstrate the state-of-the-art performance of our method on public benchmarks. The resources are available at https://github.com/ZZZHANG-jx/Marior for further comparison., Comment: This paper has been accepted by ACM Multimedia 2022

Text removal has attracted increasingly attention due to its various applications on privacy protection, document restoration, and text editing. It has shown significant progress with deep neural network. However, most of the existing methods often generate inconsistent results for complex background. To address this issue, we propose a Contextual-guided Text Removal Network, termed as CTRNet. CTRNet explores both low-level structure and high-level discriminative context feature as prior knowledge to guide the process of background restoration. We further propose a Local-global Content Modeling (LGCM) block with CNNs and Transformer-Encoder to capture local features and establish the long-term relationship among pixels globally. Finally, we incorporate LGCM with context guidance for feature modeling and decoding. Experiments on benchmark datasets, SCUT-EnsText and SCUT-Syn show that CTRNet significantly outperforms the existing state-of-the-art methods. Furthermore, a qualitative experiment on examination papers also demonstrates the generalization ability of our method. The codes and supplement materials are available at https://github.com/lcy0604/CTRNet.

End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. However, recent state-of-the-art methods usually incorporate detection and recognition simply by sharing the backbone, which does not directly take advantage of the feature interaction between the two tasks. In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer encoder with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism to explicitly guide text localization through recognition loss. The straightforward design results in a concise framework that requires neither additional rectification module nor character-level annotation for the arbitrarily-shaped text. Qualitative and quantitative experiments on multi-oriented datasets RoIC13 and ICDAR 2015, arbitrarily-shaped datasets Total-Text and CTW1500, and multi-lingual datasets ReCTS (Chinese) and VinText (Vietnamese) demonstrate SwinTextSpotter significantly outperforms existing methods. Code is available at https://github.com/mxin262/SwinTextSpotter., Comment: Accepted to be appeared in CVPR 2022

Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure. Code and model are publicly available at https://github.com/jpWang/LiLT., Comment: ACL 2022 Main conference

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.

A desire for sustainability, enjoyment of activities and financial gain, which has sparked a growing interest among researchers and businesses, drives the sharing economy phenomenon. The sharing economy enables people to sell services through reputable online platforms such as Uber or Airbnb. This research focuses on Airbnb, a peer-topeer (P2P) internet platform provider that has become one of the most successful models in the sharing economy. Because of the unique lodging experience that Airbnb users pursue, understanding Airbnb users’ perceptions of service quality and satisfaction by referring to the standards of traditional hotel customers can often be misleading. Therefore, more research is needed to explore Airbnb customers’ experience. Online reviews serve as the data source for this research, which provides a representative sample of individual customers’ personal and unique lodging experiences. This research is divided into two parts, Study 1 and Study 2. The primary purpose of Study 1 is to explore the Airbnb service quality attributes that are essential to deliver an outstanding customer experience. A novel structural topic model (STM) is employed due to its advantage that enables us to incorporate covariates in the analysis. Study 1 employed STM to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. This study complements the lack of Airbnb-related research in developing countries and improves our understanding of the Airbnb service quality attributes in Malaysia. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by mapping identified service quality attributes to five service quality dimensions of this questionnaire, which contributes to the further modification of this instrument to suit the Airbnb context. By employing the methodological advantage of STM, this study extends previous studies by examining the different preferences of Malaysian and international Airbnb users. The results reconfir

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.

A desire for sustainability, enjoyment of activities and financial gain, which has sparked a growing interest among researchers and businesses, drives the sharing economy phenomenon. The sharing economy enables people to sell services through reputable online platforms such as Uber or Airbnb. This research focuses on Airbnb, a peer-topeer (P2P) internet platform provider that has become one of the most successful models in the sharing economy. Because of the unique lodging experience that Airbnb users pursue, understanding Airbnb users’ perceptions of service quality and satisfaction by referring to the standards of traditional hotel customers can often be misleading. Therefore, more research is needed to explore Airbnb customers’ experience. Online reviews serve as the data source for this research, which provides a representative sample of individual customers’ personal and unique lodging experiences. This research is divided into two parts, Study 1 and Study 2. The primary purpose of Study 1 is to explore the Airbnb service quality attributes that are essential to deliver an outstanding customer experience. A novel structural topic model (STM) is employed due to its advantage that enables us to incorporate covariates in the analysis. Study 1 employed STM to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. This study complements the lack of Airbnb-related research in developing countries and improves our understanding of the Airbnb service quality attributes in Malaysia. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by mapping identified service quality attributes to five service quality dimensions of this questionnaire, which contributes to the further modification of this instrument to suit the Airbnb context. By employing the methodological advantage of STM, this study extends previous studies by examining the different preferences of Malaysian and international Airbnb users. The results reconfir

Visual information extraction (VIE) has attracted increasing attention in recent years. The existing methods usually first organized optical character recognition (OCR) results into plain texts and then utilized token-level entity annotations as supervision to train a sequence tagging model. However, it expends great annotation costs and may be exposed to label confusion, and the OCR errors will also significantly affect the final performance. In this paper, we propose a unified weakly-supervised learning framework called TCPN (Tag, Copy or Predict Network), which introduces 1) an efficient encoder to simultaneously model the semantic and layout information in 2D OCR results; 2) a weakly-supervised training strategy that utilizes only key information sequences as supervision; and 3) a flexible and switchable decoder which contains two inference modes: one (Copy or Predict Mode) is to output key information sequences of different categories by copying a token from the input or predicting one in each time step, and the other (Tag Mode) is to directly tag the input sequence in a single forward pass. Our method shows new state-of-the-art performance on several public benchmarks, which fully proves its effectiveness., Comment: IJCAI2021

Kai-qi Ding,* Ze-hua Lai,* Yu Zhang, Guo-yuan Yang, Ji-rong He, Li-li Zeng Department of Neurology and Institute of Neurology, Ruijin Hospital/Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Li-li Zeng No. 197 Ruijin Second Road, Huangpu District, Shanghai, 200025, People’s Republic of ChinaTel +86-13816290607Email llzeng@126.comJi-rong He No. 149 Chongqing South Road, Huangpu District, Shanghai, People’s Republic of ChinaTel +86-13162762045Email kyo3shao@aliyun.comPurpose: To explore the relationship between the monocyte-to-lymphocyte ratio (MLR) and depression three months after acute ischemic stroke.Patients and Methods: From May 2013 to September 2014, 203 patients with acute ischemic stroke were recruited within 7 days post-stroke from Shanghai Ruijin Hospital and blood samples were collected after admission. The Hamilton Depression Scale and Clinical Review were evaluated at 3 months after stroke. Based on the Diagnostic and Statistical Manual of Mental Disorders-IV diagnostic criteria, we divided patients into post-stroke depression (PSD) and non-PSD groups. We analyzed the intergroup difference in MLR and the contributing factors. Moreover, dynamic changes in monocytes, lymphocytes and MLR at four different time intervals for all the stroke patients and their relationship with PSD patients were also studied.Results: The NIHSS scores and MLR in the PSD group were significantly higher than in the non-PSD group (p< 0.05). Logistic regression analysis revealed MLR was an independent risk factor for PSD (odds ratio: 18.020, 95% confidence interval: 1.127‒288.195, p=0.041). MLR correlated negatively with cholesterol and low-density lipoprotein (r=− 0.160 and − 0.165, respectively, p< 0.05). Within 7 days post-acute ischemic stroke, monocytes gradually increased while lymphocytes remaine

Xuan-qiang Tu,1,* Ze-hua Lai,1,* Yu Zhang,2 Kai-qi Ding,1 Fei-yue Ma,3 Guo-Yuan Yang,1 Ji-rong He,3 Li-li Zeng1 1Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China; 2Department of Neurology and Institute of Neurology, Ruijin Hospital North, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 3Department of Neurology and Institute of Neurology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Li-li Zeng No. 197 Ruijin Second Road, Huangpu District, Shanghai, 200025, People’s Republic of ChinaTel +86-13816290607Email llzeng@126.comJi-rong He No. 149 Chongqing South Road, Huangpu District, Shanghai, People’s Republic of ChinaTel +86-13162762045Email kyo3shao@aliyun.comObjective: This study aimed to explore the correlation between white matter hyperintensity (WMH) and post-stroke depression (PSD) at 3 months, and to further investigate sex differences in the pathogenesis of PSD.Methods: A total of 238 consecutive patients with acute cerebral infarction were recruited. PSD was assessed at 2 weeks and at 3 months after stroke onset. All stroke cases were divided into four subgroups according to the diagnosis of depression at two time nodes: continuous depression; depression remission; late-onset PSD; and continuous non-depression. The Fazekas and Scheltens visual rating scales were adopted to assess WMH.Results: Logistic regression revealed that the presence of periventricular white matter hyperintensity (PVWMH) at baseline in male patients was an independent risk factor for PSD at 3 months. Further subgroup analysis revealed that PVWMH was associated with late-onset PSD in males, but not with continuous depression 3 months after stroke. Male acute stroke pa

Establishing robust connectivity in heterogeneous networks (HetNets) is an important yet challenging problem. For a heterogeneous network accommodating a large number of nodes, establishing perturbation-invulnerable connectivity is of utmost importance. This dissertation provides a robust advantaged node placement strategy best suited for sparse network graphs. In order to offer connectivity robustness, this work models the communication range of an advantaged node with a hexagon embedded within a circle representing the physical range of a node. Consequently, the proposed node placement method in this dissertation is based on a so-called hexagonal coordinate system (HCS) in which we develop an extended algebra. We formulate a class of geometric distance optimization problems aiming at establishing robust connectivity of a graph of multiple clusters of nodes. After showing that our formulated problem is NP-hard, we provide a heuristic algorithm based on HCS that efficiently solves the problem. Performance evaluation on different aspects of the algorithm is given. The results show that our algorithm is most effective in sparse networks for which we derive classification thresholds.After connectivity is established, we consider the problem that when some nodes are exposed to mobility, how to maintain connectivity. In this part, the network of interest consists of two types of nodes, pre-deployed (client) and intermediate nodes. We assume full control on the intermediate nodes but not the pre-deployed nodes. In such networks, on which we have only partial control, two types of node mobility scenarios are investigated. The first scenario analyzes the bounds of mobility when pre-deployed nodes move at small scales. The bounds of node mobility preserving connectivity are derived through analysis and verified by simulations.The second scenario considers the movement of pre-deployed nodes beyond the bounds of the first scenario thereby breaking connected links and partition

Establishing robust connectivity in heterogeneous networks (HetNets) is an important yet challenging problem. For a heterogeneous network accommodating a large number of nodes, establishing perturbation-invulnerable connectivity is of utmost importance. This dissertation provides a robust advantaged node placement strategy best suited for sparse network graphs. In order to offer connectivity robustness, this work models the communication range of an advantaged node with a hexagon embedded within a circle representing the physical range of a node. Consequently, the proposed node placement method in this dissertation is based on a so-called hexagonal coordinate system (HCS) in which we develop an extended algebra. We formulate a class of geometric distance optimization problems aiming at establishing robust connectivity of a graph of multiple clusters of nodes. After showing that our formulated problem is NP-hard, we provide a heuristic algorithm based on HCS that efficiently solves the problem. Performance evaluation on different aspects of the algorithm is given. The results show that our algorithm is most effective in sparse networks for which we derive classification thresholds.After connectivity is established, we consider the problem that when some nodes are exposed to mobility, how to maintain connectivity. In this part, the network of interest consists of two types of nodes, pre-deployed (client) and intermediate nodes. We assume full control on the intermediate nodes but not the pre-deployed nodes. In such networks, on which we have only partial control, two types of node mobility scenarios are investigated. The first scenario analyzes the bounds of mobility when pre-deployed nodes move at small scales. The bounds of node mobility preserving connectivity are derived through analysis and verified by simulations.The second scenario considers the movement of pre-deployed nodes beyond the bounds of the first scenario thereby breaking connected links and partition

Розвиток науково-технічного прогресу в машинобудуванні, енергетиці, авіакосмічній та інших галузях промисловості немислимий без створення нових конструкційних матеріалів, здатних поліпшити найважливіші параметри двигунів, машин і механізмів, агрегатів, приладів, підвищити їх масові показники, надійність, термін служби виробів і знизити їх матеріаломісткість. Вироби і агрегати з полімерних композиційних матеріалів (ПКМ) можна отримати різними способами. У промисловому виробництві найбільшого поширення набули методи намотування і пресування. Пресування може здійснюватися з використанням суміші попередньо нарубаного волокна і полімерного сполучного. Одним із найбільш багатообіцяючих методів формування виробів і агрегатів з різноманітного пластика виступає метод намотування волокном, за рахунок того, що він створює необхідну структуру наповнювача у фабрикатів залежно від їх форми й особливостей експлуатації. У статті описано сфери ефективного застосування полімерних композиційних матеріалів. Розглянуто технологічні можливості і галузі застосування традиційних і нових різновидів способу. Наведено результати аналізу перспектив розвитку технологій виготовлення виробів і агрегатів з полімерних композиційних матеріалів за допомогою методу сухого та мокрого намотування. Намотування здійснюється на оправлення, встановлене на верстат із числовим програмним управлінням. Цей метод має також назву методу спірального намотування. Викладено існуючі підходи до розроблення математичних моделей, створених для виготовлення складнопрофільних елементів конструкцій, в тому числі виробів і агрегатів з вигнутою просторовою віссю., Развитие научно-технического прогресса в машиностроении, энергетике, авиакосмической и других отраслях промышленности немыслимо без создания новых конструкционных материалов, способных улучшить важнейшие параметры двигателей, машин и механизмов, агрегатов, приборов, повысить их массовые показатели, надежность, срок службы изделий и снизить их материалоемкость. Изделия и агрегаты из полимерных композиционных материалов (ПКМ) могут быть получены различными способами. В промышленном производстве наибольшее распространение получили методы намотки и прессования. Прессование может осуществляться с использованием смеси предварительно нарубленного волокна и полимерного связующего. Одним из самых многообещающих методов формования изделий и агрегатов из разнообразного пластика выступает метод намотки волокном, за счет того, что он создает требуемую структуру наполнителя в фабрикатах в зависимости от их формы и особенностей эксплуатации. В статье описаны области эффективного применения полимерных композиционных материалов. Рассмотрены технологические возможности и области применения традиционных и новых разновидностей способа. Приведены результаты анализа перспектив развития технологий изготовления изделий и агрегатов из полимерных композиционных материалов с помощью метода сухой и мокрой намотки. Изложены существующие подходы к разработке математических моделей, созданных для изготовления сложно-профильных элементов конструкций, в том числе изделий и агрегатов с изогнутой пространственной осью., The development of scientific and technological progress in mechanical engineering, energy, aerospace and other industries is unthinkable without the creation of new structural materials that can improve the most important parameters of engines, machines and mechanisms, assemblies, devices and increase their mass performance, reliability, service life of products and reduce their material consumption. Products and aggregates from polymer composite materials (PCM) can be obtained in various ways. In industrial production, the most widely used method of winding and pressing. Pressing can be carried out using a mixture of preliminarily fiber and a polymer binder. One of the most promising methods of forming products and assemblies from a variety of plastic is the method of winding with fiber, due to the fact that it creates the required filler structure in the factories depending on their shape and operation characteristics. All areas of effective use of polymer composite materials are reflected. This article discusses the technological capabilities and applications of traditional and new varieties of the method. Even, the results of an analysis of the prospects for the development of manufacturing technologies for products and aggregates from polymer composite materials using the winding method are presented. There are methods of dry and wet winding. Consider these winding methods. Winding is carried out on a mandrel mounted on a machine with numerical control. This method is also called the spiral winding method. Existing approaches to the development of mathematical models are described and created for the manufacture of complex structural elements, including products and an assembly with a curved spatial axis, and so on.

Today, Deep Learning (DL) enhances almost every industrial sector, including safety-critical areas. The next generation of safety standards will define appropriate verification techniques for DL-based applications and propose adequate fault tolerance mechanisms. DL-based applications, like any other software, are susceptible to common random hardware faults such as bit flips, which occur in RAM and CPU registers. Such faults can lead to silent data corruption. Therefore, it is crucial to develop methods and tools that help to evaluate how DL components operate under the presence of such faults. In this paper, we introduce two new Fault Injection (FI) frameworks InjectTF and InjectTF2 for TensorFlow 1 and TensorFlow 2, respectively. Both frameworks are available on GitHub and allow the configurable injection of random faults into Neural Networks (NN). In order to demonstrate the feasibility of the frameworks, we also present the results of FI experiments conducted on four VGG-based Convolutional NNs using two image sets. The results demonstrate how random bit flips in the output of particular mathematical operations and layers of NNs affect the classification accuracy. These results help to identify the most critical operations and layers, compare the reliability characteristics of functionally similar NNs, and introduce selective fault tolerance mechanisms., Comment: Preprint of the corresponding ESREL 2020 PSAM15 conference publication

Розвиток науково-технічного прогресу в машинобудуванні, енергетиці, авіакосмічній та інших галузях промисловості немислимий без створення нових конструкційних матеріалів, здатних поліпшити найважливіші параметри двигунів, машин і механізмів, агрегатів, приладів, підвищити їх масові показники, надійність, термін служби виробів і знизити їх матеріаломісткість. Вироби і агрегати з полімерних композиційних матеріалів (ПКМ) можна отримати різними способами. У промисловому виробництві найбільшого поширення набули методи намотування і пресування. Пресування може здійснюватися з використанням суміші попередньо нарубаного волокна і полімерного сполучного. Одним із найбільш багатообіцяючих методів формування виробів і агрегатів з різноманітного пластика виступає метод намотування волокном, за рахунок того, що він створює необхідну структуру наповнювача у фабрикатів залежно від їх форми й особливостей експлуатації. У статті описано сфери ефективного застосування полімерних композиційних матеріалів. Розглянуто технологічні можливості і галузі застосування традиційних і нових різновидів способу. Наведено результати аналізу перспектив розвитку технологій виготовлення виробів і агрегатів з полімерних композиційних матеріалів за допомогою методу сухого та мокрого намотування. Намотування здійснюється на оправлення, встановлене на верстат із числовим програмним управлінням. Цей метод має також назву методу спірального намотування. Викладено існуючі підходи до розроблення математичних моделей, створених для виготовлення складнопрофільних елементів конструкцій, в тому числі виробів і агрегатів з вигнутою просторовою віссю., Развитие научно-технического прогресса в машиностроении, энергетике, авиакосмической и других отраслях промышленности немыслимо без создания новых конструкционных материалов, способных улучшить важнейшие параметры двигателей, машин и механизмов, агрегатов, приборов, повысить их массовые показатели, надежность, срок службы изделий и снизить их материалоемкость. Изделия и агрегаты из полимерных композиционных материалов (ПКМ) могут быть получены различными способами. В промышленном производстве наибольшее распространение получили методы намотки и прессования. Прессование может осуществляться с использованием смеси предварительно нарубленного волокна и полимерного связующего. Одним из самых многообещающих методов формования изделий и агрегатов из разнообразного пластика выступает метод намотки волокном, за счет того, что он создает требуемую структуру наполнителя в фабрикатах в зависимости от их формы и особенностей эксплуатации. В статье описаны области эффективного применения полимерных композиционных материалов. Рассмотрены технологические возможности и области применения традиционных и новых разновидностей способа. Приведены результаты анализа перспектив развития технологий изготовления изделий и агрегатов из полимерных композиционных материалов с помощью метода сухой и мокрой намотки. Намотка осуществляется на оправку, установленную на станок с числовым программным управлением. Этот метод носит также название метода спиральной намотки. Изложены существующие подходы к разработке математических моделей созданных для изготовления сложнопрофильных элементов конструкций, в том числе изделий и агрегатов с изогнутой пространственной осью., The development of scientific and technological progress in mechanical engineering, energy, aerospace and other industries is unthinkable without the creation of new structural materials that can improve the most important parameters of engines, machines and mechanisms, assemblies, devices and increase their mass performance, reliability, service life of products and reduce their material consumption. Products and aggregates from polymer composite materials (PCM) can be obtained in various ways. In industrial production, the most widely used method of winding and pressing. Pressing can be carried out using a mixture of preliminarily fiber and a polymer binder. One of the most promising methods of forming products and assemblies from a variety of plastic is the method of winding with fiber, due to the fact that it creates the required filler structure in the factories depending on their shape and operation characteristics. All areas of effective use of polymer composite materials are reflected. This article discusses the technological capabilities and applications of traditional and new varieties of the method. Even, the results of an analysis of the prospects for the development of manufacturing technologies for products and aggregates from polymer composite materials using the winding method are presented. There are methods of dry and wet winding. Consider these winding methods. Winding is carried out on a mandrel mounted on a machine with numerical control. This method is also called the spiral winding method. Existing approaches to the development of mathematical models are described and created for the manufacture of complex structural elements, including products and an assembly with a curved spatial axis, and so on.

Model-based design is a common methodology in the development of embedded complex control systems. Control system engineers typically prefer to use MATLAB® Simulink® and suitable automatic code generators for the development and deployment of software. Embedded systems are subject to random hardware faults; bit-flips, for example, may affect random access memory (RAM) cells and central processing unit (CPU) registers and cause data errors that may propagate to critical system outputs and result in system failures. From a dependability perspective, the design space of control systems includes the selection of a suitable (reliable) implementation of a control algorithm. Such algorithm can be implemented with model-based software development frameworks, such as Simulink using different, but functionally equivalent implementations. However, these functional equivalents may exhibit completely different reliability properties. This thesis proposes an analytical method for the evaluation of the reliability properties of control systems that are designed with Simulink models. The method is based on a transformation of the assembly code, which is generated from the Simulink model, into a formal stochastic error propagation model as well as its quantification through underlying Markov chain models and state-of-the-art probabilistic model-checking techniques. The application of the method to the functionally equivalent implementations can determine which one is less vulnerable to data errors due to random hardware faults. Fault tolerance is significant to dependable system design. Control systems can be protected with fault tolerance mechanisms to increase the reliability. Redundancy is the key underlying concept for achieving fault tolerance that is usually implemented at the hardware or software level. In the case of model-based development, redundancy mechanisms are preferable for direct application at the model level (Simulink model level). This thesis introduces a systema

Model-based design is a common methodology in the development of embedded complex control systems. Control system engineers typically prefer to use MATLAB® Simulink® and suitable automatic code generators for the development and deployment of software. Embedded systems are subject to random hardware faults; bit-flips, for example, may affect random access memory (RAM) cells and central processing unit (CPU) registers and cause data errors that may propagate to critical system outputs and result in system failures. From a dependability perspective, the design space of control systems includes the selection of a suitable (reliable) implementation of a control algorithm. Such algorithm can be implemented with model-based software development frameworks, such as Simulink using different, but functionally equivalent implementations. However, these functional equivalents may exhibit completely different reliability properties. This thesis proposes an analytical method for the evaluation of the reliability properties of control systems that are designed with Simulink models. The method is based on a transformation of the assembly code, which is generated from the Simulink model, into a formal stochastic error propagation model as well as its quantification through underlying Markov chain models and state-of-the-art probabilistic model-checking techniques. The application of the method to the functionally equivalent implementations can determine which one is less vulnerable to data errors due to random hardware faults. Fault tolerance is significant to dependable system design. Control systems can be protected with fault tolerance mechanisms to increase the reliability. Redundancy is the key underlying concept for achieving fault tolerance that is usually implemented at the hardware or software level. In the case of model-based development, redundancy mechanisms are preferable for direct application at the model level (Simulink model level). This thesis introduces a systema

Model-based design is a common methodology in the development of embedded complex control systems. Control system engineers typically prefer to use MATLAB® Simulink® and suitable automatic code generators for the development and deployment of software. Embedded systems are subject to random hardware faults; bit-flips, for example, may affect random access memory (RAM) cells and central processing unit (CPU) registers and cause data errors that may propagate to critical system outputs and result in system failures. From a dependability perspective, the design space of control systems includes the selection of a suitable (reliable) implementation of a control algorithm. Such algorithm can be implemented with model-based software development frameworks, such as Simulink using different, but functionally equivalent implementations. However, these functional equivalents may exhibit completely different reliability properties. This thesis proposes an analytical method for the evaluation of the reliability properties of control systems that are designed with Simulink models. The method is based on a transformation of the assembly code, which is generated from the Simulink model, into a formal stochastic error propagation model as well as its quantification through underlying Markov chain models and state-of-the-art probabilistic model-checking techniques. The application of the method to the functionally equivalent implementations can determine which one is less vulnerable to data errors due to random hardware faults. Fault tolerance is significant to dependable system design. Control systems can be protected with fault tolerance mechanisms to increase the reliability. Redundancy is the key underlying concept for achieving fault tolerance that is usually implemented at the hardware or software level. In the case of model-based development, redundancy mechanisms are preferable for direct application at the model level (Simulink model level). This thesis introduces a systema

Model-based design is a common methodology in the development of embedded complex control systems. Control system engineers typically prefer to use MATLAB® Simulink® and suitable automatic code generators for the development and deployment of software. Embedded systems are subject to random hardware faults; bit-flips, for example, may affect random access memory (RAM) cells and central processing unit (CPU) registers and cause data errors that may propagate to critical system outputs and result in system failures. From a dependability perspective, the design space of control systems includes the selection of a suitable (reliable) implementation of a control algorithm. Such algorithm can be implemented with model-based software development frameworks, such as Simulink using different, but functionally equivalent implementations. However, these functional equivalents may exhibit completely different reliability properties. This thesis proposes an analytical method for the evaluation of the reliability properties of control systems that are designed with Simulink models. The method is based on a transformation of the assembly code, which is generated from the Simulink model, into a formal stochastic error propagation model as well as its quantification through underlying Markov chain models and state-of-the-art probabilistic model-checking techniques. The application of the method to the functionally equivalent implementations can determine which one is less vulnerable to data errors due to random hardware faults. Fault tolerance is significant to dependable system design. Control systems can be protected with fault tolerance mechanisms to increase the reliability. Redundancy is the key underlying concept for achieving fault tolerance that is usually implemented at the hardware or software level. In the case of model-based development, redundancy mechanisms are preferable for direct application at the model level (Simulink model level). This thesis introduces a systema

Redundancy mechanisms such as triple modular redundancy protect safety-critical components by replication and thus improve systems fault tolerance. However, the gained fault tolerance comes along with costs to be invested, e.g., increasing execution time, energy consumption, or packaging size, for which constraints have to be obeyed during system design. This turns the question of finding suitable combinations of components to be protected into a challenging task as the number of possible protection combinations grows exponentially in the number of components. We propose family-based approaches to tackle the combinatorial blowup in redundancy systems modeling and analysis phases. Based on systems designed in SIMULINK we show how to obtain models that include all possible protection combinations and present a tool chain that, given a probabilistic error model, generates discrete Markov chain families. Using symbolic techniques that enable concise family representation and analysis, we show how SIMULINK models of realistic size can be protected and analyzed with a single family-based analysis run while a one-by-one analysis of each protection combination would clearly exceed any realistic time constraints., Comment: This paper is a preprint of the corresponding ESREL'19 conference publication

In autonomous navigation, a planning system reasons about other agents to plan a safe and plausible trajectory. Before planning starts, agents are typically processed with computationally intensive models for recognition, tracking, motion estimation and prediction. With limited computational resources and a large number of agents to process in real time, it becomes important to efficiently rank agents according to their impact on the decision making process. This allows spending more time processing the most important agents. We propose a system to rank agents around an autonomous vehicle (AV) in real time. We automatically generate a ranking data set by running the planner in simulation on real-world logged data, where we can afford to run more accurate and expensive models on all the agents. The causes of various planner actions are logged and used for assigning ground truth importance scores. The generated data set can be used to learn ranking models. In particular, we show the utility of combining learned features, via a convolutional neural network, with engineered features designed to capture domain knowledge. We show the benefits of various design choices experimentally. When tested on real AVs, our system demonstrates the capability of understanding complex driving situations., Comment: 8 pages, accepted to IEEE/RSJ International Conference on Robots and Systems (IROS) 2019

Liming is a common agronomic practice used for alleviating soil acidification to improve plant growth. However, it is still unclear how liming can affect the gut microbiota composition of soil fauna, and subsequently the nutrient cycling and litter decomposition mediated by soil fauna. In the present study the effect of liming on the gut microbiota of two types of soil fauna, Folsomia candida, and Enchytraeus crypticus was investigated by using 16S rRNA gene high-throughput sequencing. The results revealed that there are differences between the gut microbial communities of the two types of soil fauna as well as between the gut microbiome of the soil fauna and the surrounding soil. Enterobacteriaceae and Bacillaceae were the predominant families in the gut microbiota of E. crypticus, while Rickettsiaceae and Moraxellaceae were the predominant families in the gut microbiota of F. candida. Liming affected the gut microbiota of E. crypticus at both the taxonomical and core microbiota level. The gut microbiota of F. candida was not affected by liming. Structural equation models suggest that 97% of the variation in the E. crypticus gut microbiota could be explained by liming-induced changes in soil properties and the soil microbial community. The indirect effects of liming, caused by a shift in the soil microbial community, contributed more in reshaping the gut microbiota of E. crypticus than the direct effects of the changed soil properties did. These findings suggest that the effects of liming on the gut microbiota composition in soil fauna are species-specific and are likely dependent on the response of the host to changes in soil properties and the soil microbial community.

Steven R Sarkisian,1 Basil Mathews,2 Kai Ding,2 Aashka Patel,2 Zachary Nicek2 1University of Oklahoma College of Medicine, Dean McGee Eye Institute, Oklahoma City, OK, USA; 2University of Oklahoma, Oklahoma City, OK, USA Purpose: The purpose of this study was to evaluate the safety and efficacy of microinvasive glaucoma surgery (MIGS) with 360° ab-interno trabeculotomy using the TRAB360 device as a stand-alone procedure in patients with refractory primary open-angle glaucoma (POAG) and preoperative IOP ≥18 mmHg.Setting: This study evaluated patients treated in a tertiary-referral clinical practice setting.Design: This study is a retrospective analysis of 81 eyes.Methods: Patients with refractory open-angle glaucoma underwent stand-alone 360° ab-interno trabeculotomy using the TRAB360 device. Effectiveness was determined by reduction in medicated IOP and the use of medications from baseline. Safety was determined by the rate of adverse events and secondary surgical interventions. The time points assessed were baseline and postoperative day 1, week 1, and months 1, 3, 6, and 12. A subgroup analysis was performed on eyes with medicated preoperative IOP values of ≥25 mmHg.Results: The reductions in IOP from 1 day to 12 months postoperatively were statistically significant compared to baseline values. The mean reduction in IOP at 12 months was 7.3±6.7 mmHg from baseline. At 12 months, 59% eyes achieved ≥20% reduction in IOP and IOP <18 mmHg with the same or fewer numbers of IOP-lowering medications compared with those at baseline. The mean number of IOP-lowering medications was reduced from 1.7±1.3 at baseline to 1.1±1.0 at 12 months. At 12 months, 67% of eyes with preoperative IOP values of ≥25 mmHg achieved ≥20% reduction in IOP and IOP <18 mmHg with the same or fewer numbers of IOP-lowering medications compared with those at baseline. The most common adverse event for all

Time-lapse microscopy is a powerful, versatile and constantly developing tool for real-time imaging of living cells. This review outlines the advances of time-lapse microscopy and refers to the most interesting reports, thus pointing at the fact that the modern biology and medicine are entering the thrilling and promising age of molecular cinematography.

Establishing robust connectivity in heterogeneous networks (HetNets) is an important yet challenging problem. For a HetNet accommodating a large number of nodes, establishing perturbation-invulnerable connectivity is of utmost importance. This paper provides a robust advantaged node placement strategy best suited for sparse network graphs. In order to offer connectivity robustness, this paper models the communication range of an advantaged node with a hexagon embedded within a circle representing the physical range of a node. Consequently, the proposed node placement method of this paper is based on a so-called hexagonal coordinate system (HCS) in which we develop an extended algebra. We formulate a class of geometric distance optimization problems aiming at establishing robust connectivity of a graph of multiple clusters of nodes. After showing that our formulated problem is NP-hard, we utilize HCS to efficiently solve an approximation of the problem. First, we show that our solution closely approximates an exhaustive search solution approach for the originally formulated NP-hard problem. Then, we illustrate its advantages in comparison with other alternatives through experimental results capturing advantaged node cost, runtime, and robustness characteristics. The results show that our algorithm is most effective in sparse networks for which we derive classification thresholds., Comment: 14 pages, 11 figures, IEEE Transactions on Network Science and Engineering 2017

Zhi-Wei Lu,1,2,* Ming-Yu Du,2,* Lu-Xi Qian,1,2 Nan Zhang,2 Jia-Jia Gu,2 Kai Ding,3 Jing Wu,2 Hong-Ming Zhu,2 Xia He,1,2 Li Yin1,2 1The Fourth Clinical School of Nanjing Medical University, Nanjing, Jiangsu, China; 2Department of Radiation Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, Jiangsu, China; 3Department of Radiation Oncology, Suqian First Hospital, Suqian, Jiangsu, China *These authors contributed equally to this work Background: In recent years, miR-152 has been dysregulated in a variety of tumors and used as a tumor suppressor. Nevertheless, its role in nasopharyngeal carcinoma (NPC) remains unidentified.Materials and methods: Real-time quantitative PCR (polymerase chain reaction) was performed to analyze the expression of miR-152 in NPC cell lines. MiR-152 expression profiles in NPC tissues were obtained from Gene Expression Omnibus (GEO GSE36682). The effect of miR-152 on the invasion and proliferation of NPC cells was determined through cell invasion, wound healing, and cell viability assays. Apoptosis was examined by flow cytometry, and Western blot was performed to measure expression of the target gene. Pyrosequencing was used to detect the methylation level of NPC cells.Results: In this study, miR-152 was downregulated in the NPC tissues and cell lines. When miR-152 was enhanced, the invasion and migration of NPC cells were inhibited. However, miR-152 had no effect on the proliferation of NPC cells. Luciferase reporter gene analysis was performed, and the results showed that DNMT1 (DNA (cytosine-5)-methyltransferase 1) is a direct target of miR-152 in NPC cells. DNMT1 downregulation and miR-152 overexpression both reversed the effects of miR-152 inhibition on the NPC cells. In addition, miR-152 expression increased as a result of the inhibition of the methylation level of miR-152 when DNMT1 expression was downregulated.Conclusion: The overexpression of miR-152

Inorganic phosphate solubilizing bacteria (iPSB) are essential to facilitate phosphorus (P) mobilization in alkaline soil, however, the phylogenetic structure of iPSB communities remains poorly characterized. Thus, we use a reference iPSB database to analyze the distribution of iPSB communities based on 16S rRNA gene illumina sequencing. Additionally, a noval pqqC primer was developed to quantify iPSB abundance. In our study, an alkaline soil with 27-year fertilization treatment was selected. The percentage of iPSB was 1.10~2.87% per sample, and the dominant iPSB genera were closely related to Arthrobacter, Bacillus, Brevibacterium and Streptomyces. Long-term P fertilization had no significant effect on the abundance of iPSB communities. Rather than P and potassium (K) additions, long-term nitrogen (N) fertilization decreased the iPSB abundance, which was validated by reduced relative abundance of pqqC gene (pqqC/16S). The decreased iPSB abundance was strongly related to pH decline and total N increase, revealing that the long-term N additions may cause pH decline and subsequent P releases relatively decreasing the demands of the iPSB community. The methodology and understanding obtained here provides insights into the ecology of inorganic P solubilizers and how to manipulate for better P use efficiency.

Inorganic phosphate solubilizing bacteria (iPSB) are essential to facilitate phosphorus (P) mobilization in alkaline soil, however, the phylogenetic structure of iPSB communities remains poorly characterized. Thus, we use a reference iPSB database to analyze the distribution of iPSB communities based on 16S rRNA gene illumina sequencing. Additionally, a noval pqqC primer was developed to quantify iPSB abundance. In our study, an alkaline soil with 27-year fertilization treatment was selected. The percentage of iPSB was 1.10~2.87% per sample, and the dominant iPSB genera were closely related to Arthrobacter, Bacillus, Brevibacterium and Streptomyces. Long-term P fertilization had no significant effect on the abundance of iPSB communities. Rather than P and potassium (K) additions, long-term nitrogen (N) fertilization decreased the iPSB abundance, which was validated by reduced relative abundance of pqqC gene (pqqC/16S). The decreased iPSB abundance was strongly related to pH decline and total N increase, revealing that the long-term N additions may cause pH decline and subsequent P releases relatively decreasing the demands of the iPSB community. The methodology and understanding obtained here provides insights into the ecology of inorganic P solubilizers and how to manipulate for better P use efficiency.

El uso del marketing personal, para conseguir empleo, es crucial. Este tipo de promoción individual es primordial y debe tenerse en cuenta a la hora de establecer relaciones laborales a mediano o largo plazo. La tesina hace un repaso del empleo del blog para la mejora de la marca personal en internet, mejorando la búsqueda de empleo en la red, The use of personal marketing, to obtain employment, is very important nowadays. This kind of individual promotion is paramount and must be taken into account when setting the medium to long term working relationships. The thesis gives an overview of the use of blog to improve personal branding online, improving job search at the internet

El uso del marketing personal, para conseguir empleo, es crucial. Este tipo de promoción individual es primordial y debe tenerse en cuenta a la hora de establecer relaciones laborales a mediano o largo plazo. La tesina hace un repaso del empleo del blog para la mejora de la marca personal en internet, mejorando la búsqueda de empleo en la red, The use of personal marketing, to obtain employment, is very important nowadays. This kind of individual promotion is paramount and must be taken into account when setting the medium to long term working relationships. The thesis gives an overview of the use of blog to improve personal branding online, improving job search at the internet

El uso del marketing personal, para conseguir empleo, es crucial. Este tipo de promoción individual es primordial y debe tenerse en cuenta a la hora de establecer relaciones laborales a mediano o largo plazo. La tesina hace un repaso del empleo del blog para la mejora de la marca personal en internet, mejorando la búsqueda de empleo en la red, The use of personal marketing, to obtain employment, is very important nowadays. This kind of individual promotion is paramount and must be taken into account when setting the medium to long term working relationships. The thesis gives an overview of the use of blog to improve personal branding online, improving job search at the internet

This paper described a novel design of a hybrid fuel cell and energy storage system using high temperature superconducting energy storage system (HT-SMES) or batteries to meet fast changing load. The power electronic switches in the converter of the energy storage system are controlled in such a way that the amplitude and waveform of the inverter output current from the fuel cell is tightly regulated to follow a sinusoidal reference waveform. In this way, the energy storage system is used to supply the fast changing part of the load and the fuel cell system continuously supplies a constant full load demand. The SABER simulation results during motor starting will be presented and discussed.

This paper described a novel design of a hybrid fuel cell and energy storage system using high temperature superconducting energy storage system (HT-SMES) or batteries to meet fast changing load. The power electronic switches in the converter of the energy storage system are controlled in such a way that the amplitude and waveform of the inverter output current from the fuel cell is tightly regulated to follow a sinusoidal reference waveform. In this way, the energy storage system is used to supply the fast changing part of the load and the fuel cell system continuously supplies a constant full load demand. The SABER simulation results during motor starting will be presented and discussed.

This paper described a novel design of a hybrid fuel cell and energy storage system using high temperature superconducting energy storage system (HT-SMES) or batteries to meet fast changing load. The power electronic switches in the converter of the energy storage system are controlled in such a way that the amplitude and waveform of the inverter output current from the fuel cell is tightly regulated to follow a sinusoidal reference waveform. In this way, the energy storage system is used to supply the fast changing part of the load and the fuel cell system continuously supplies a constant full load demand. The SABER simulation results during motor starting will be presented and discussed.

This paper described a novel design of a hybrid fuel cell and energy storage system using high temperature superconducting energy storage system (HT-SMES) or batteries to meet fast changing load. The power electronic switches in the converter of the energy storage system are controlled in such a way that the amplitude and waveform of the inverter output current from the fuel cell is tightly regulated to follow a sinusoidal reference waveform. In this way, the energy storage system is used to supply the fast changing part of the load and the fuel cell system continuously supplies a constant full load demand. The SABER simulation results during motor starting will be presented and discussed.

As an environmentally-friendly aquaculture and planting system, aquaponics has attracted attention in various fields, such as fisheries, agriculture, and ecology. The existing review qualitatively described the development and challenges of aquaponics but lacked data support. This study selected 513 related documents (2000-2019) in the Web of Science database (WOS) to mine and quantitatively analyze its text data. The keyword co-occurrence network shows that the current aquaponics research mainly focuses on the system components, wastewater treatment, nutrient management, and system production. Research areas reflect obvious regional characteristics. China, the United States and Europe are dedicated to the application of new technologies, the optimization of system production, and the exploration of multiple roles. At present, the aquaponics development is facing many pressures from management and market. Future research requires more in-depth research in the system construction, nutrient management, and microbial community structure to provide a theoretical basis. Moreover, the identity construction within the conceptual framework of green infrastructure is a research direction worth exploring to solve low social recognition for aquaponics.

As an environmentally-friendly aquaculture and planting system, aquaponics has attracted attention in various fields, such as fisheries, agriculture, and ecology. The existing review qualitatively described the development and challenges of aquaponics but lacked data support. This study selected 513 related documents (2000-2019) in the Web of Science database (WOS) to mine and quantitatively analyze its text data. The keyword co-occurrence network shows that the current aquaponics research mainly focuses on the system components, wastewater treatment, nutrient management, and system production. Research areas reflect obvious regional characteristics. China, the United States and Europe are dedicated to the application of new technologies, the optimization of system production, and the exploration of multiple roles. At present, the aquaponics development is facing many pressures from management and market. Future research requires more in-depth research in the system construction, nutrient management, and microbial community structure to provide a theoretical basis. Moreover, the identity construction within the conceptual framework of green infrastructure is a research direction worth exploring to solve low social recognition for aquaponics.