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This paper proposes a new method that uses Alexnet with ImageNet transfer learning as the feature extractor and optimized and regularized extreme learning as the classifier. We keep the first five convolutional layers and the first two fully connected layers of Alexnet, and then train the network. Then, the mutual information between each dimension of the feature and its category is calculated and sorted, and the feature with the highest ranking is selected for feature dimensionality reduction. The regularization penalty term is introduced to the extreme learning machine to control its algorithm complexity and solve the problem of overfitting. Finally, the Runge Kutta optimization algorithm is employed to ameliorate the hidden layer bias and input weight of the regularized extreme learning machine, and the optimized regularized extreme learning machine is used to classify the dimensionality-reduced clothing image traits. The test outcome illustrates that on some apparel classification with style (ACWS) datasets, the precision, recall, F1-score, and accuracy of the proposed algorithm are 93.06%, 93.17%, 92.82%, and 93.14%, respectively, which are better than those of other clothing image classification algorithms. The results verify that the raised algorithm significantly ameliorates the classification property of clothing image algorithms.

A labyrinthine-domain-structure pattern, contributing to the high piezoelectric performance, is observed via a PFM in K0.41Na0.59Ta0.41Nb0.59O3 with a polymorphic phase transition boundary.

Cloud computing has emerged as a popular technology platform that allows businesses to store, access, and process data and applications over the internet. This technology has the potential to reduce costs, increase scalability, and improve efficiency, making it an attractive option for businesses of all sizes. However, as the adoption of cloud computing continues to grow, concerns have been raised about its environmental impact. This has led to the need for an assessment of the environmental sustainability of cloud computing. In this research, we take a life cycle assessment approach to assess the environmental sustainability of cloud computing. The life cycle assessment approach involves the evaluation of the environmental impact of a product or service throughout its entire life cycle, from the extraction of raw materials to the disposal of waste. We apply this approach to cloud computing by analyzing the environmental impact of the hardware, software, and data center infrastructure required to support cloud computing. Our research shows that cloud computing can have a significant environmental impact, particularly in terms of energy consumption and carbon emissions. The hardware required to support cloud computing, such as servers and data storage devices, consumes a significant amount of energy and contributes to greenhouse gas emissions. Additionally, the cooling systems required to maintain optimal temperatures in data centers also consume a significant amount of energy. We further identify several strategies that can be implemented to improve the environmental sustainability of cloud computing. These strategies include the use of renewable energy sources to power data centers, the optimization of data center cooling systems, and the use of energy-efficient hardware and software. Our findings highlight the need for businesses and policymakers to consider the environmental impact of cloud computing when making decisions about technology adoption. By implementing strategies to improve the environmental sustainability of cloud computing, businesses can reduce their carbon footprint and contribute to a more sustainable future.

Floating nuclear power plant (FNPP) is a movable nuclear power plant built on the floating platform, which can provide clean and stable power for remote coastal areas, and are currently a hot research topic in the field of nuclear power. The steel containment is located in the reactor compartment of the FNPP and it is an important safety guarantee structure. Fatigue and fracture have been an important issue for ship and offshore structures for a long time. Fatigue failure of containment will have serious consequences.In order to research the fatigue life analysis method of steel containment of the first FNPP in China, the paper adopts miner linear cumulative damage theory and spectral analysis method, based on the American Society of Mechanical Engineers (ASME) standards and relevant standards of China Classification Society (CCS), and uses AQWA to analyze Wave load of FNPP. The hydrodynamic calculation results are imported into finite element model to analyze the structural response of each point of containment, and calibrate the transfer function data of each key point by using the linear system theory and regular wave periodic evaluation method. The fatigue analysis of each point is carried out according to the transfer function and the wave dispersion diagram drawn by the forty years monitoring sea conditions of the working sea area of the FNPP. The result shows that the fatigue life of steel containment is superior and meets the service requirements.

Surface defect passivation, which plays a vital role in achieving high-efficiency perovskite solar cells (PSCs) in a spin-coating process, is rarely compatible with a printing process. Currently, printing PSCs with high efficiency remains a challenge, as only a few laboratories realized an efficiency of over 20%. In this work, zwitterionic compounds 2-hydroxyethyl trimethyl ammonium chloride (HETACl) and butyltrimethylammonium chloride (BTACl) were introduced, both of which can spontaneously adsorb on the surface perovskite and form an ultrathin passivation layer by a dip coating method. The complex formed by the strong interaction of HETACl with MAI on the surface of the perovskite film leads to the formation of a rough perovskite surface, which affects the enhancement of device performance. BTACl with a chemically inert side chain induces a weak interaction with the perovskite. It is demonstrated that BTACl not only passivates surface defects of the perovskite but also heals the grain boundaries and results in more uniform crystallizations. Finally, PSCs upon BTACl treatment were blade-coated in an ambient environment with a relative humidity of50%, which produced a champion efficiency of 20.5% with negligible hysteresis, and the active area of the cell device was 0.095 cm

Individuals with social anxiety disorder (iSAD) experience adverse outcomes in daily life due to the disorder (e.g., lower educational and work achievement compared to their healthy counterparts). They are prone to social isolation, even though they desire intimate interpersonal relationships. Yet, little research on iSAD is devoted to understanding in detail a) when they interpret social situations as social-anxiety-provoking, b) how this interpretation motivates their assessments of their efficacy and likely interaction outcomes, and c) how they choose specific information-seeking strategies in uncertain social situations. Leveraging the theory of motivated information management (TMIM) and the emotional systems (ES) model, we explored the lived experiences of iSAD. We conducted in-depth interviews (N = 27) and analyzed them using thematic analysis. iSAD perceived discrepancies in a) mutual goals, b) common ground, and c) self-image as social-anxiety-provoking. These interpretations motivated their assessments of a) socializing benefits, b) communication competency, and c) interactant partner's amiability, which led to a mix of information-seeking decisions in social situations. Practical and theoretical implications for future research are discussed.

Over the past two decades, a number of advances in topic modeling have produced sophisticated models that are capable of generating topic hierarchies. In particular, hierarchical Latent Dirichlet Allocation (hLDA) builds a topic tree based on the nested Chinese Restaurant Process (nCRP) or other sampling processes to generate a topic hierarchy that allows arbitrarily large branch structures and adaptive dataset growth. In addition, hierarchical topic models based on the latent tree model, such as Hierarchical Latent Tree Analysis (HLTA), have been developed over the last five years. However, these models do not work well in cases with millions of documents and hundreds of thousands of terms. In addition, the topic trees generated by these models are always poorly interpretable, and the relationships among topics in different levels are relatively simple. The biomedical literature, including Medline abstracts, has large-scale documents in two major categories: biological laboratory research and medical clinical research. We propose a top-down binary hierarchical topic model (biHTM) for biomedical literature by iteratively applying a flat topic model and adaptively processing subtrees of the hierarchy. The biHTM topic hierarchy of complete Medline abstracts with more than 14 topic node levels shows good bimodality and interpretability. Compared to hLDA and HLTA, biHTM shows promising results in experiments assessed in terms of runtime and quality.

Coastal wetlands are an important source of methane emissions, and understanding the mechanisms that control methane emissions from coastal wetlands is of great significance to global warming. Anaerobic oxidation of methane driven by sulfate is an important process to prevent methane emissions from coastal wetlands. The effects of environmental changes on this process and the function of the sulfate-methane transition zone (SMTZ) are poorly understood. In this study, spatiotemporal variations in pore-water geochemistry (concentrations of SO

Visual Simultaneous Localization And Mapping (V-SLAM) and object detection algorithms are two critical prerequisites for modern XR applications. V-SLAM allows XR devices to geometrically map the environment and localize itself within the environment, simultaneously. Furthermore, object detectors based on Deep Neural Network (DNN) can be used to semantically understand what those features in the environment represent. However, both of these algorithms are computationally expensive, which makes it challenging for them to achieve good real-time performance on device. In this thesis, we first present TensoRT Quantized YOLOv4 (TRTQYOLOv4), a faster implementation of YOLOv4 architecture [1] using FP16 reduced precision and INT8 quantization powered by NVIDIA TensorRT [2] framework. Second, we propose the V-SLAM Hurdler: A Faster VSLAM System using Online Dynamic-and-Hardness-aware Approximation. The proposed system integrates the base RGB-D V-SLAM ORB-SLAM3 [3] with the INT8 TRTQ-YOLOv4 object detector, a novel Entropy-based Degreeof- Difficulty Estimator, an Online Hardness-aware Approximation Controller and a Dynamic Object Eraser, applying online dynamic-and-hardness aware approximation to the base V-SLAM system during runtime while increasing its robustness in dynamic scenes. We first evaluate the proposed object detector on public object detection dataset. The proposed FP16 precision TRTQ-YOLOv4 achieves 2×faster than the full-precision model without loss of accuracy, while the INT8 quantized TRTQ-YOLOv4 is almost 3×faster than the full-precision one with only 0.024 loss in mAP@50:5:95. Second, we evaluate our proposed V-SLAM system on public RGB-D SLAM dataset. In static scenes, the proposed system speeds up the base VSLAM system by +21.2% on average with only −0.7% loss of accuracy. In dynamic scenes, the proposed system not only accelerate the base system by +23.5% but also improves the accuracy by +89.3%, making it as robust as in the static scenes. Lastly, the comparison against the state-of-the-art SLAMs designed dynamic environments shows that our system outperforms most of the compared methods in highly dynamic scenes. Visual SLAM (V-SLAM) och objektdetekteringsalgoritmer är två kritiska förutsättningar för moderna XR-applikationer. V-SLAM tillåter XR-enheter att geometriskt kartlägga miljön och lokalisera sig i miljön samtidigt. Dessutom kan DNN-baserade objektdetektorer användas för att semantiskt förstå vad dessa egenskaper i miljön representerar. Men båda dessa algoritmer är beräkningsmässigt dyra, vilket gör det utmanande för dem att uppnå bra realtidsprestanda på enheten. I det här examensarbetet presenterar vi först TRTQ-YOLOv4, en snabbare implementering av YOLOv4 arkitektur [1] med FP16 reducerad precision och INT8 kvantisering som drivs av NVIDIA TensorRT [2] ramverk. För det andra föreslår vi V-SLAM-häckaren: ett snabbare V-SLAM-system som använder online-dynamisk och hårdhetsmedveten approximation. Det föreslagna systemet integrerar basen RGB-D V-SLAM ORB-SLAM3 [3] med INT8 TRTQYOLOv4 objektdetektorn, en ny Entropi-baserad svårighetsgradsuppskattare, en online hårdhetsmedveten approximationskontroller och en Dynamic Object Eraser, applicerar online-dynamik- och hårdhetsmedveten approximation till bas-V-SLAM-systemet under körning samtidigt som det ökar dess robusthet i dynamiska scener. Vi utvärderar först den föreslagna objektdetektorn på datauppsättning för offentlig objektdetektering. Den föreslagna FP16 precision TRTQ-YOLOv4 uppnår 2× snabbare än fullprecisionsmodellen utan förlust av noggrannhet, medan den INT8 kvantiserade TRTQ-YOLOv4 är nästan 3× snabbare än fullprecisionsmodellen med endast 0.024 förlust i mAP@50:5:95. För det andra utvärderar vi vårt föreslagna V-SLAM-system på offentlig RGB-D SLAM-datauppsättning. I statiska scener snabbar det föreslagna systemet upp V-SLAM-bassystemet med +21.2% i genomsnitt med endast −0.7% förlust av noggrannhet. I dynamiska scener accelererar det föreslagna systemet inte bara bassystemet med +23.5% utan förbättrar också noggrannheten med +89.3%, vilket gör det lika robust som i de statiska scenerna. Slutligen visar jämförelsen med de senaste SLAM-designade dynamiska miljöerna att vårt system överträffar de flesta av de jämförda metoderna i mycket dynamiska scener.

An end-to-end deep learning model based on the deep belief network (DBN) and gated recurrent unit (GRU) is proposed to recognize the single disturbance events and composite disturbance events in the phase-sensitive optical time-domain reflectometer ( φ -OTDR). Making use of the DBN to fit the original data, five kinds of single disturbance events can be effectively recognized with the GRU network as the classifier. An average recognition accuracy of 96.72% with a short recognition time of 0.079 s can be achieved for single disturbance events. Moreover, the proposed method is also applied for recognizing composite disturbance events. Four kinds of composite disturbance events can be recognized with an average recognition accuracy as high as 90.94%, and the corresponding recognition time is only 0.084 s. Up until now, there have been fewer reports about the recognition of composite disturbance events in φ -OTDR systems. High recognition accuracy and short recognition time make the model based on DBN-GRU more capable in a high sensitivity, real-time φ -OTDR system.

The problem of potentially toxic elements (PTEs) in farmland is a key issue in global pollution prevention and control and has an important impact on environmental safety, human health, and sustainable agricultural development. Based on the climate background of high-latitude cold regions, this study simulated freeze-thaw cycles through indoor tests. Different initial conditions, such as biochar application rates (0%, 1%, 2%) and different initial soil moisture contents (15%, 20%, 25%), were set to explore the morphological changes in cadmium (Cd) and lead (Pb) in soil and the response relationship to the changes in soil physicochemical properties. The results indicate that soil pH decreases during freeze-thaw cycles, and soil alkalinity increases with increasing biochar content. Freeze-thaw cycles caused the total amount of PTEs to have a U-shaped distribution, and the amount of PTEs in the soluble (SOL) and reducible (RED) fraction increased by 0.28-56.19%. Biochar reduced the amount of Cd and Pb migration in the soil, and an increase in soil moisture content reduced the availability of Cd and Pb in the soil. Freezing and thawing damaged the soil structure, and biochar reduced the fractionation of small particle aggregates by enhancing the stability of soil aggregates, thereby reducing the soil's ability to adsorb Cd and Pb. In summary, for farmland soil remediation and pollution control, the application of biochar has a certain ability to optimize soil properties. Considering the distribution of PTEs in the soil and the physicochemical properties of the soil, the application of 1% biochar to soil with a 20% moisture content is optimal for regulating seasonally frozen soil remediation.

Understanding of the binding characteristics of wetland dissolved organic matter (DOM) and different metals is important for the quantitative assessment of the environmental behavior of metals in wetlands. In this study, different types of spectroscopy including ultraviolet-visible absorption, Fourier transform infrared, and fluorescence spectroscopy was used to investigate the binding characteristics of Fe

BACKGROUND Death and disability from coronary heart disease (CHD) can be largely reduced by improving risk factor management. However, adhering to evidence-based recommendations is challenging and requires interventions at the level of the patient, provider, and health system. OBJECTIVE The aim of this study was to develop an Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) to facilitate adherence to health behaviors and preventive medications, and to test the usability of iCARE. METHODS We developed iCARE based on a user-centered design approach, which included 4 phases: (1) function design, (2) iterative design, (3) expert inspections and walkthroughs of the prototypes, and (4) usability testing with end users. The usability testing of iCARE included 2 stages: stage I, which included a task analysis and a usability evaluation (January to March 2019) of the iCARE patient app using the modified Health Information Technology Usability Survey (Health-ITUES); and stage II (June 2020), which used the Health-ITUES among end users who used the app for 6 months. The end users were individuals with a confirmed diagnosis of CHD from 2 university-affiliated hospitals in Beijing, China. RESULTS iCARE consists of a patient app, a care provider app, and a cloud platform. It has a set of algorithms that trigger tailored feedback and can send individualized interventions based on data from initial assessment and health monitoring via manual entry or wearable devices. For stage I usability testing, 88 hospitalized patients (72% [63/88] male; mean age 60 [SD 9.9] years) with CHD were included in the study. The mean score of the usability testing was 90.1 (interquartile range 83.3-99.0). Among enrolled participants, 90% (79/88) were satisfied with iCARE; 94% (83/88) and 82% (72/88) reported that iCARE was useful and easy to use, respectively. For stage II usability testing, 61 individuals with CHD (85% [52/61] male; mean age 53 [SD 8.2] years) who were from an intervention arm and used iCARE for at least six months were included. The mean total score on usability testing based on the questionnaire was 89.0 (interquartile distance: 77.0-99.5). Among enrolled participants, 89% (54/61) were satisfied with the use of iCARE, 93% (57/61) perceived it as useful, and 70% (43/61) as easy to use. CONCLUSIONS This study developed an intelligent, individualized, evidence-based, and theory-driven app (iCARE) to improve patients’ adherence to health behaviors and medication management. iCARE was identified to be highly acceptable, useful, and easy to use among individuals with a diagnosis of CHD. CLINICALTRIAL Chinese Clinical Trial Registry ChiCTR-INR-16010242; https://tinyurl.com/2p8bkrew

Fake base station (FBS) has been exploited by criminals to attack mobile users by spamming fraudulent messages for over a decade. Despite that prior work has proposed several techniques to mitigate this issue, FBS spam is still a long-standing challenging issue in some countries, such as China, and causes billions of dollars of financial loss every year. Therefore, understanding and exploring the thematic strategies in the FBS spam ecosystem at a large scale would improve the defense mechanisms. In this paper, we present the first large-scale characterization of FBS spam ecosystem by collecting three-month real-world FBS detection results. First, at "macro-level'', we uncover the characteristics of FBS spammers, including their business categories, temporal patterns and spatial patterns. Second, at "micro-level'', we investigate how FBS ecosystem is organized and how fraudulent messages are constructed by campaigns to trap users and evade detection. Collectively, the results expand our understanding of the FBS spam ecosystem and provide new insights into improved mitigation mechanisms for the security community.

By studying the basic design principles of human-computer natural interaction and the related theories of three-dimensional interactive gestures, aiming at the problems of lack of theoretical guidance and unclear usability level of gesture interaction design in the field of virtual assembly, the gesture usability evaluation index model oriented towards virtual assembly is constructed based on the Analytic Hierarchy Process. Using usability theory as a guide, analyzing usability influencing factors, summarizing usability evaluation indicators, calculating the importance of each usability indicator, and designing and evaluating the rationality of different gesture sets to verify the indicators and weight distribution, this article provides data support and theoretical basis for the design of 3D interactive gestures for virtual assembly.

Natural language processing (NLP) models are known vulnerable to adversarial examples, similar to image processing models. Studying adversarial texts is an essential step to improve the robustness of NLP models. However, existing studies mainly focus on analyzing English texts and generating adversarial examples for English texts. There is no work studying the possibility and effect of the transformation to another language, e.g, Chinese. In this paper, we analyze the differences between Chinese and English, and explore the methodology to transform the existing English adversarial generation method to Chinese. We propose a novel black-box adversarial Chinese texts generation solution Argot, by utilizing the method for adversarial English samples and several novel methods developed on Chinese characteristics. Argot could effectively and efficiently generate adversarial Chinese texts with good readability. Furthermore, Argot could also automatically generate targeted Chinese adversarial text, achieving a high success rate and ensuring readability of the Chinese.

The contents of modern medicine are various and complicated, so clinicians need to master more about medical knowledge. This paper introduces a medical process visualization clinical medical decision support system (CDSS) oriented to NCCN clinical practice guidelines, which can assist clinicians to make diagnosis and treatment decisions. And a new knowledge representation model is designed for NCCN guidelines. The model can express medical knowledge freely and flexibly, and medical knowledge also can be updated in time. At present, medical process visualization CDSS has been implemented for many different cancers.

The popularity of online gambling could bring negative social impact, and many countries ban or restrict online gambling. Taking China for example, online gambling violates Chinese laws and hence is illegal. However, illegal online gambling websites are still thriving despite strict restrictions, since they are able to make tremendous illicit profits by trapping and cheating online players. In this paper, we conduct the first deep analysis on illegal online gambling targeting Chinese to unveil its profit chain. After successfully identifying more than 967,954 suspicious illegal gambling websites, we inspect these illegal gambling websites from five aspects, including webpage structure similarity, SEO (Search Engine Optimization) methods, the abuse of Internet infrastructure, third-party online payment, and gambling group. Then we conduct a measurement study on the profit chain of illegal online gambling, investigating the upstream and downstream of these illegal gambling websites. We mainly focus on promotion strategies, third-party online payment, the abuse of third-party live chat services, and network infrastructures. Our findings shed the light on the ecosystem of online gambling and help the security community thwart illegal online gambling.

Covalent anchoring of 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) on the scaffold of conjugated microporous polymers (CMPs) was achieved via cost-effective FeCl3-catalyzed oxidative polymerization of dicarbazole functionalized TEMPO. These TEMPO-CMPs exhibit excellent catalytic performance for the aerobic oxidation of a wide variety of alcohols under metal-free conditions. In addition, the TEMPO-CMPs could be recycled at least 20 times with good retention of high catalytic activities.

The need satisfaction and psychological benefits derived from gameplay are generally understudied for older video game players. This study connects the Self-Determination Theory, Motivational Theory of Life-Span Development, and Socioemotional Selectivity Theory to understand players’ in-game behaviors and their corresponding need satisfaction from a developmental perspective. Survey data from 1213 randomly sampled World of Tanks players were combined with their behavioral data to investigate how players’ behaviors and their corresponding need satisfaction differ or converge across age. Age and in-game behaviors were tested as moderators for the relationship between perceived need satisfaction and psychological well-being. The results showed that despite underperforming and having fewer in-game connections, older players reported no significant difference in their perceived competence and relatedness than younger players. Perceived competence and relatedness contributed to psychological well-being for both older and younger players, although it carried more weight for the younger. Theoretical and practical implications are discussed.

Understanding of the binding characteristics of wetland dissolved organic matter (DOM) and different metals is important for the quantitative assessment of the environmental behavior of metals in wetlands. In this study, different types of spectroscopy including ultraviolet-visible absorption, Fourier transform infrared, and fluorescence spectroscopy was used to investigate the binding characteristics of Fe3+, Cu2+, Cr3+, Cd2+, and Zn2+ with DOM from wetland water. Differential absorption spectra identified binding sites for these five metals in this wetland DOM at 210 nm, 280 nm, 335 nm, and > 400 nm regions. The low binding capacity of DOM in this wetland with Cd and Zn indicated that the toxicity and environmental effects of these metals in this wetland warrant further study. The calculated △EEM combined with fluorescence regional integration (FRI) analysis clearly revealed that Fe and Cu preferred to bind with humic-like DOM while Cd and Zn preferred to bind with protein-like DOM in this wetland. △EEM successfully demonstrated the characteristics of DOM complexing with different metals and could be a compelling tool in evaluating metal-DOM interactions. In addition, 2D-FTIR-COS identified the binding sites and the dynamic processes of binding at the functional group level. Metals preferentially bind with the C O, C O functional group, and then binds to the O H functional group. This study revealed that different DOM components will facilitate the migration of different metals in the environment and provided new slights into an improved understanding of migration and transformation of metals in aquatic environments.

Under iodine-catalyzed and visible light-irradiated aerobic conditions, selenoalkoxylation of olefins with diselenides and alcohols can be efficiently achieved to afford the useful α -alkoxyl selenides in the presence of only 0.5 equiv. of H2O2. Controlling the sub-stoichiometric H2O2 amount is crucial to avoid the non-selective over-oxidation of the diselenides that leads to the ineffective hyper-valent selenium compounds. Meanwhile, under visible light irradiation, the green, safe, and low-cost air can work as a supplemental mild oxidant in the reaction to ensure selective oxidation of the diselenides, full conversion of the reactants, and ultimately good yield of the products.

As smartphone’s computing power continues to grow and as mobile applications (apps) continue to dominate digital engagement, apps have become a new frontier for advancing field experiment methodology. Using apps may help researchers to scale up the reach, precisely control randomization and experiment materials, collect a variety of objective and self-reported data over time, and more conveniently replicate and adapt an experiment. We performed a systematic review on field experiments involving apps published between 2007 and 2017. Seven databases were scanned using a predefined search strategy. The database search retrieved 4,810 citations; 101 articles met the inclusion criteria. Our review suggests that scholars have only started to employ apps in field experiments in the last 4 years. Most studies only used apps as an experiment treatment instead of an experiment platform; therefore, researchers have yet to fully leverage the advantages. Almost all studies were from the health research domain and 77.2% used randomized controlled trial design. Only 7 studies utilized smartphone sensors for collecting data. Only one study reported cost and ethical concerns regarding using apps for the experiment. Given these findings, we reported a case study that targeted a minority racial group and leveraged the advantages of apps as an experiment platform and as a data collection tool to illustrate practical challenges and lessons learned regarding time, financial cost, and technical support. In conclusion, we suggest apps provide new ways to study causal mechanisms with experiment big data. Limitations of generalizability, retention, and design quality were discussed as well.

Herein, we report two novel Anderson-type polyoxometalate (POM) built-in conjugated microporous polymers (CMPs), Bn-Anderson-CMP and Th-Anderson-CMP prepared through Sonogashira–Hagihara cross-coupling of tetrabromo-bifunctionalized Anderson-type POMs and 1,3,5-triethynylbenzene. These two Anderson-CMPs exhibit outstanding heterogeneous photocatalytic activities towards degrading organic dyes in water. Control photocatalysis experiments with different radical scavengers demonstrate that hydrogen peroxide and singlet oxygen are the primary active catalytic species. Moreover, these two CMPs can be easily recycled at least five times without a noticeable decrease in photocatalytic performances.

In this paper, we present a large-scale analysis about an emerging new type of domain-name fraud, which we call levelsquatting. Unlike existing frauds that impersonate well-known brand names (like google.com) by using similar second-level domain names, adversaries here embed brand name in the subdomain section, deceiving users especially mobile users who do not pay attention to the entire domain names.

A clear understanding of factors governing stable isotopic variations in precipitation of tropical cyclones is critical for constraining atmospheric hydrological model simulations. The temporal and spatial variations in stable isotopic compositions of precipitation during the typhoon Lekima (2019) were investigated, based on rainwater samples collected at four sampling sites along its track between August 10 and August 12, 2019. Results showed that the δ18O and δD values of rainwater samples varied from -15.5‰ to -2.9‰ and from -112.4‰ to -17.3‰, respectively. The large ranges of δ18O and δD values in rainwater from the typhoon Lekima were most likely caused by the changes in rainfall intensity and its complex interaction with local water vapor. In addition, it was observed that the δ18O and δD values gradually decreased from the outer rainbands to the inner rainbands, and their values were more depleted of heavy isotopes than those of local rain. We speculated that both the high stratiform precipitation fractions and the deep convection system may be responsible for the isotopic depletion of rainwater related with the typhoon Lekima. It reveals that the rain type fractions and the intensity of convection should be considered in the elucidation of δ18O signals in extreme precipitation events. This study also has important implications for understanding atmospheric moisture cycles in tropical cyclones.

The discovery of a series of novel organic reactions has made methylenecyclopropanes (MCPs) some of the most popular building blocks in synthetic organic chemistry during the past two decades. Among reported works, the construction of heterocycles from MCPs has highlighted new synthetic methodologies that afford more opportunities for the quick synthesis of elaborately substituted products, and this should draw a great deal of attention. However, reviews in this area are insufficient, and the latest monograph on heterocycle synthesis from MCPs was published 12 years ago. This review aims to summarize the novel organic reactions of MCPs to produce heterocycles published in recent years, which have provided specific and powerful tools for organic synthesis.

A new ion-imprinted poly(ethyleneimine)(PEI)-functionalized silica sorbents were synthesized by a hydrothermal-assisted surface grafting technique with Pb(II) as the template, PEI as the functional molecular, and epichlorohydrin as the cross-linking agent for the selective adsorption of Pb(II) from aqueous solution, and was characterized by FTIR, SEM, TGA-DTA, nitrogen adsorption, and the static batch experiment. Static adsorption experiment results showed that Pb(II)-imprinted PEI-functionalized silica sorbents had high static adsorption capacity of 60.4 mg g−1, reached an equilibrium state within 20 min, displayed stable adsorption ability for Pb(II) ions in the range of pH 4–8, had satisfactory selectivity toward Pb(II) and could be used repeatedly. Compared with Freundlich and Dubinin–Radushkevich isotherms, equilibrium data fitted to Langmuir adsorption model. The kinetic process of adsorption followed a pseudo-second-order model compared to pseudo-first-order, Elovich and intraparticle diffu...

The discovery of a series of novel organic reactions has made methylenecyclopropanes (MCPs) some of the most popular building blocks in synthetic organic chemistry during the past two decades. Among reported works, the construction of heterocycles from MCPs has highlighted new synthetic methodologies that afford more opportunities for the quick synthesis of elaborately substituted products, and this should draw a great deal of attention. However, reviews in this area are insufficient, and the latest monograph on heterocycle synthesis from MCPs was published 12 years ago. This review aims to summarize the novel organic reactions of MCPs to produce heterocycles published in recent years, which have provided specific and powerful tools for organic synthesis.

Bladder dysfunction caused by spinal cord injury is one of the most difficult clinical problems not yet solved. It often causes severe urinary incontinence, urinary retention and urinary tract infection, or even renal failure leading to death [1, 2]. Exploration of ways to solve this problem is very important to the improvement of paraplegic patients’ quality of life and reduction of their mortality rate. Urodynamic examination has an exact value in the diagnosis, classification, treatment and prognosis of lower urinary tract dysfunction of patients with spinal cord injury. It can classify bladders, thereby providing timely objective indicators on which to base the diagnosis and treatment of patients with lower urinary tract dysfunction. We performed a systematic examination of lower urinary tract function of 136 patients with spinal cord injury, established a relatively detailed and systematic classification and set treatment principles correspondingly.

Localized surface plasmon resonance (LSPR)-based sensing has found wide applications in medical diagnosis, food safety regulation and environmental monitoring. Compared with commercial propagating surface plasmon resonance (PSPR)-based sensors, LSPR ones are simple, cost-effective and suitable for measuring local refractive index changes. However, the figure of merit (FOM) values of LSPR sensors are generally 1-2 orders of magnitude smaller than those of PSPR ones, preventing the widespread use of LSPR sensors. Here we describe an array of submicrometer gold mushrooms with a FOM reaching ~108, which is comparable to the theoretically predicted upper limit for standard PSPR sensors. Such a high FOM arises from the interference between Wood's anomaly and the LSPRs. We further demonstrate the array as a biosensor for detecting cytochrome c and alpha-fetoprotein, with their detection limits down to 200 pM and 15 ng ml(-1), respectively, suggesting that the array is a promising candidate for label-free biomedical sensing.

This paper presents a Chinese short text classification method which considering extended semantic constraints and statistical constraints. This method uses “HowNet” tools to build the attribute set of concept. when coming to the part of feature expansion, we judge the collocation between the attribute words of original text and the characteristics before and after expansion as the semantic constraints, and calculate the ratio between the mutual information of the original contents and the features before expansion versus the mutual information of the original contents and the features after expansion as statistical constraints, so as to judge whether feature expansion is effective with this two constraints , then rationally use various semantic relation word-pairs in short text classification. Experiments show that this method can use semantic relations in Chinese short text classification effectively, and improve the classification performance.

To observe the effect of selective sacral rhizotomy in treating spastic bladder after spinal cord injury and to explore the mechanism and the best surgical method of different sacral rhizotomies.The spastic bladder models were established in 12 male dogs and were divided into 4 groups according to the different rhizotomies of the sacral nerve as the following: rhizotomy of the anterior root of S2 (group A), rhizotomy of the anterior root of S2 and half of the anterior root of S3 (group B), rhizotomy of the anterior roots of S2 and S3 (group C), and total rhizotomy of the nerve roots of S2-4 (group D). By urodynamic examination and electrophysiological observation, the changes of all functional data were recorded and compared between pre-rhizotomy and post-rhizotomy to testify the best surgical method. In clinical trial, according to the results of the above experiments, rhizotomy of the anterior root of S2 or one of the half-anterior root of S3 were conducted on 32 patients with spastic bladder after spinal cord injury. The mean bladder capacity, the mean urine evacuation and the mean urethra pressure were (120 +/- 30), (100 +/- 30)ml and (120 +/- 20) cm H2O, respectively before rhizotomy.After rhizotomy, the bladder capacity in 4 groups amounted to (150 +/- 50), (180 +/- 50), (230 +/- 50), and (400 +/- 50) ml, respectively; and the urine evacuation volume were (130 +/- 30), (180 +/- 50), (100 +/- 50) and (50 +/- 30)ml, respectively. In the treated 32 patients, the mean bladder capacity were raised to 410 ml, and the mean urine evacuation volume were also increased to 350 ml. Incontinence of urine disappeared in all patients. After 22-month follow-up on 13 patients, no recurrence was observed.The effect of selective sacral rhizotomy in treating spastic cord injury is significant and worthy of further studies.