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With the support of advanced hardware and software technology, Artificial Intelligence (AI) techniques, especially the increasing number of deep learning algorithms, have spawned the popularization of online intelligent services and accelerated the contemporary development and applications of chatbot systems. The promise of providing 24/7 uninterrupted business services and minimizing workforce costs has made business chatbots a hot topic due to the impact of the pandemic. It has attracted considerable attention from academic researchers and business practitioners. However, a thorough technical review of advanced chatbot technologies and their relevance and applications to various business domains is rare in the literature. The main contribution of this review article is the critical analysis of various chatbot development approaches and the underlying deep learning computational methods in the context of some business applications. We first conceptualize current business chatbot architectures and illustrate the technical characteristics of two common structures. Next, we explore the mainstream deep learning technologies in chatbot design from the perspective of computational methods and usages. Then, we propose a new framework to classify chatbot construction architectures and differentiate the traditional retrieval-based and generation-based chatbots in terms of the modern pipeline and end-to-end structures. Finally, we highlight future research directions for business chatbots to enable researchers to devote their efforts to the most promising research topics and commercial scenarios and for practitioners to benefit from realizing the trend in business chatbot development and applications. [ABSTRACT FROM AUTHOR]

The single cleavage drilled compression specimens of sandstone were impacted by the large-diameter split Hopkinson pressure bar, during which the whole model-I dynamical fracture process was successfully observed. A crack propagation gauge is used to monitor the key time moment of dynamic initiation, propagation, arrest and re-initiation, respectively. The fractal crack extension model is used to analysis the propagation speed of the tortuous crack, and with further combination of the experimental–numerical-analytical method, to determine the dynamic initiation toughness, dynamic propagation toughness, dynamic arrest toughness, and dynamic re-initiation toughness of sandstone. The results show that in the process of crack propagation, the crack propagation path is torturous; and for this curved path, the value of the universal function, which is characterized by the crack's velocity, is smaller than that with a straight path. The dynamic propagation toughness thus obtained is closer to its real value by using the fractal model. Sandstone's dynamic initiation toughness is greater than the dynamic arrest toughness, and the dynamic initiation toughness is slightly bigger than the dynamic re-initiation toughness. [ABSTRACT FROM AUTHOR]

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20–25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections. [ABSTRACT FROM AUTHOR]

Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field. [ABSTRACT FROM AUTHOR]

Attributed graph clustering, the task of grouping nodes into communities using both graph structure and node attributes, is a fundamental problem in graph analysis. Recent approaches have utilized deep learning for node embedding followed by conventional clustering methods. However, these methods often suffer from the limitations of relying on the original network structure, which may be inadequate for clustering due to sparsity and noise, and using separate approaches that yield suboptimal embeddings for clustering. To address these limitations, we propose a novel method called Deep Attributed Clustering with High-order Proximity Preserve (DAC-HPP) for attributed graph clustering. DAC-HPP leverages an end-to-end deep clustering framework that integrates high-order proximities and fosters structural cohesiveness and attribute homogeneity. We introduce a modified Random Walk with Restart that captures k-order structural and attribute information, enabling the modelling of interactions between network structure and high-order proximities. A consensus matrix representation is constructed by combining diverse proximity measures, and a deep joint clustering approach is employed to leverage the complementary strengths of embedding and clustering. In summary, DAC-HPP offers a unique solution for attributed graph clustering by incorporating high-order proximities and employing an end-to-end deep clustering framework. Extensive experiments demonstrate its effectiveness, showcasing its superiority over existing methods. Evaluation on synthetic and real networks demonstrates that DAC-HPP outperforms seven state-of-the-art approaches, confirming its potential for advancing attributed graph clustering research. [ABSTRACT FROM AUTHOR]

Some thiazolamide derivatives containing diphenyl ether moiety were designed, synthesized, and studied for their antifungal activity and cell cytotoxicity. Of these, several compounds II possessed excellent activity against Rhizoctonia solani, which was better than that of boscalid and comparable to thifluzamide. Besides, the cytotoxicity test showed that the optimal compound IIf had lower cytotoxicity than thifluzamide in general. Further, molecular docking could give a reasonable explanation for the high activity of the target compounds. The above results indicated that thiazolamide derivatives containing diphenyl ether moiety are valuable for in-depth study to obtain high-efficiency and low-toxicity green pesticides. A series of new thiazolamide derivatives containing diphenyl ether moiety were designed and synthesized. The biological evaluation indicated optimal compound IIf displayed not only high fungicidal activity but also lower cytotoxicity. Besides, molecular docking gave a reasonable explanation for the high activity of the target compounds. [ABSTRACT FROM AUTHOR]

Background: A few studies have tested febuxostat for its usefulness in delaying chronic kidney disease (CKD) progression by treating hyperuricemia and results were controversial. Thus, we attempted to conduct a randomized controlled study using the Chinese population with advanced grade of CKD. Methods: One hundred CKD patients in stages 3 and 4 with asymptomatic hyperuricemia from seven medical centers were included in this prospective randomized controlled study and assigned to the control and febuxostat group, the latter of which received febuxostat to titrate to achieve serum uric acid (SUA) < 6 mg/dL. The observation period was 12 months. The primary outcomes included the event of estimated glomerular filtration rate (eGFR) decline ≥ 30% or 50% from baseline at 12 months, dialysis and death from CKD; secondary outcome was the change in eGFR. Safety analysis was also performed. Results: Forty-seven patients and 45 patients in the febuxostat and control groups, respectively completed the study. Seven of 47 (14.9%) participants reached 30% decline in eGFR in the febuxostat group, while 1 (2.1%) and 2 (4.3%) patients reached 50% decline in eGFR or dialysis. Thirteen (28.9%), 10 (22.2%) and 3 (6.7%) of 45 patients reached primary kidney outcomes separately in the control group. The change in eGFR after 12 months from baseline in the febuxostat group was 0.50 mL/min/1.73 m2, which was significantly higher than that in the control group − 4.46 mL/min/1.73 m2 (p = 0.006). Adverse events did not differ between two groups. Conclusions: Febuxostat effectively slowed eGFR decline in patients with CKD stages 3 and 4 and asymptomatic hyperuricemia. [ABSTRACT FROM AUTHOR]

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that affects social interaction and behavior. Mutations in the gene encoding chromodomain helicase DNA-binding protein 8 (CHD8) lead to autism symptoms and macrocephaly by a haploinsufficiency mechanism. However, studies of small animal models showed inconsistent findings about the mechanisms for CHD8 deficiency-mediated autism symptoms and macrocephaly. Using the nonhuman primate as a model system, we found that CRISPR/Cas9-mediated CHD8 mutations in the embryos of cynomolgus monkeys led to increased gliogenesis to cause macrocephaly in cynomolgus monkeys. Disrupting CHD8 in the fetal monkey brain prior to gliogenesis increased the number of glial cells in newborn monkeys. Moreover, knocking down CHD8 via CRISPR/Cas9 in organotypic monkey brain slices from newborn monkeys also enhanced the proliferation of glial cells. Our findings suggest that gliogenesis is critical for brain size in primates and that abnormal gliogenesis may contribute to ASD. [ABSTRACT FROM AUTHOR]

Link prediction is one of the most widely studied problems in the area of complex network analysis, in which machine learning techniques can be applied to deal with it. The biggest drawback of the existing methods, however, is that in most cases they only consider the topological structure of the network, and therefore completely miss out on the great potential that stems from the nodal attributes. Both topological structure and nodes' attributes are essential in predicting the evolution of attributed networks and can act as complements to each other. To bring out their full potential in solving the link prediction problem, a novel Robust Graph Regularization Nonnegative Matrix Factorization for Attributed Networks (RGNMF-AN) was proposed, which models not only the topology structure of networks but also their node attributes for direct link prediction. This model, in particular, combines two types of information, namely network topology, and nodal attributes information, and calculates high-order proximities between nodes using the Structure-Attribute Random Walk Similarity (SARWS) method. The SARWS score matrix is an indicator structural and attributed matrix that collects more useful attributed information in high-order proximities, whereas graph regularization technology combines the SARWS score matrix with topological and attribute information to collect more valuable attributed information in high-order proximities. Furthermore, the RGNMF-AN employs the ℓ2,1-norm to constrain the loss function and regularization terms, effectively removing random noise and spurious links. According to empirical findings on nine real-world complex network datasets, the use of a combination of attributed and topological information in tandem enhances the prediction performance significantly compared to the baseline and other NMF-based algorithms. [ABSTRACT FROM AUTHOR]

AAV-delivered CRISPR/Cas9 (AAV-CRISPR) has shown promising potentials in preclinical models to efficiently insert therapeutic gene sequences in somatic tissues. However, the AAV input doses required were prohibitively high and posed serious risk of toxicity. Here, we performed AAV-CRISPR mediated homology-independent knock-in at a new target site in mAlb 3'UTR and demonstrated that single dose of AAVs enabled long-term integration and expression of hF9 transgene in both adult and neonatal hemophilia B mice (mF9 −/−), yielding high levels of circulating human Factor IX (hFIX) and stable hemostasis restoration during entire 48-week observation period. Furthermore, we achieved hemostasis correction with a significantly lower AAV dose (2 × 109 vg/neonate and 1 × 1010 vg/adult mouse) through liver-specific gene knock-in using hyperactive hF9R338L variant. The plasma antibodies against Cas9 and AAV in the neonatal mice receiving low-dose AAV-CRISPR were negligible, which lent support to the development of AAV-CRISPR mediated somatic knock-in for treating inherited diseases. Here, the authors develop an AAV-CRISPR mediated somatic knock-in. They apply this system to restore hemostasis in neonatal hemophilia B mice and show liver-specificity of the knock-in and low serum antibody production. [ABSTRACT FROM AUTHOR]

Objective: The performance and stability of radiomics model caused by dimension reduction remain being confronted with major challenges. In this study, we aimed to propose a new scheme of global feature management independent of dimension reduction to improve it. Methods: The non-contrast computed tomography (NCCT) images of acute brainstem infarction (ABI) from two medical centers were used as test and validation sets. A new scheme was constructed based on global feature management, and the traditional scheme dependent on dimension reduction was used as control. The radiomic features of NCCT images were extracted in Matlab R2013a. The performance of prediction model was evaluated by the generalized linear model (GLM) and multivariate logistic regression. And, the stability of radiomics model was evaluated with the difference of area under curve (AUC) between the test and validation sets. Results: Compared with the traditional scheme, the new scheme presented a similar detection performance (AUC: 0.875 vs. 0.883), yet a better performance in predicting prognosis (AUC: 0.864, OR = 0.917, p = 0.021 vs. AUC:0.806, OR = 0.972, p = 0.007). All these results were well verified in an independent validation set. Moreover, the new scheme showed stronger stability in both the detection model (ΔAUC: 0.013 vs. 0.039) and prediction model (ΔAUC = 0.004 vs. 0.044). Conclusion: Although there might be several limitations, this study proved that the scheme of global feature management independent of dimension reduction could be a powerful supplement to the radiomics methodology. Key Points: • The new scheme (Swavelet) presented similar detection performances for ABI with the traditional scheme. • A better predictive performance for END was found in the new scheme (Swavelet) compared with the traditional scheme. • Stronger model stability was found in both the detection and prediction models based on the new scheme. [ABSTRACT FROM AUTHOR]

Objective: Although the guideline indicates that total kidney volume (TKV) is an important detection indicator in patients with autosomal dominant polycystic kidney disease (ADPKD), this study attempted to demonstrate that renal parenchymal information, combined with parenchymal volume and radiomics features, may have more valuable clinical guiding significance. Methods: A totals of 340 ADPKD patients with normal renal function were prospectively collected and followed-up for five years, with renal function tests and non-contrast computed tomography (CT) performed every six months. The relationship between renal function impairment and renal parenchymal volume (RPV) as along with radiomics features was explored using a multiple linear regression model and multiple logistic regression. Then, a combined model of RPV with radiomics features was constructed to comprehensively evaluate its predictive value. Results: Compared with TKV, decreased RPV presented a closer relationship with renal function impairment, namely, with the impairment rate (RPV: 82.3% vs. TVK: 67.1%) and eGFR (RPV: r = 0.614, p < 0.001 vs. TKV: r = -0.452, p < 0.001), and showed higher predictive power (RPV: AUC = 0.752 [95%CI 0.692–0.805], p < 0.001 vs. TKV: AUC = 0.711 [95%CI 0.649–0.768], p < 0.001). Correspondingly, the radiomics analysis that was derived from the renal parenchyma also showed a satisfactory result (AUC = 0.849 [95%Cl 0.797–0.892], p < 0.001). Importantly, the predictive power for renal function impairment was further improved in the combined model of RPV and radiomics features (AUC = 0.902 [95%Cl 0.857–0.937], p < 0.001). Conclusion: Renal parenchyma information may be a sensitive biomarker of renal function impairment in ADPKD, which can provide a new approach for clinically monitoring renal function, and may greatly improve the pre-hospital prevention and treatment effects. [ABSTRACT FROM AUTHOR]

Activated carbon (AC) was treated with nitric acid, hydrogen peroxide, and urea. The obtained products were then used as supports for the preparation of palladium/activated carbon (Pd/AC) catalysts. The characterization results revealed that the AC textural structure and surface properties were selectively modified via different treatments, and the surface functional groups of the supports could affect the particle size, dispersion, and chemical states of the Pd nanoparticles. The surface of the urea-treated AC (AC-U) contained more carbonylic groups and nitrogenous species but fewer hydroxylic and carboxylic groups than the surfaces of the ACs subjected to other treatments. Consequently, in the hydrogenation synthesis of meropenem, the Pd/AC-U catalyst provided a higher meropenem yield and lower amounts of impurities in the meropenem product than the other catalysts. The nitrogenous groups not only provided efficient anchoring sites for Pd nanoparticles, improving the reaction activity, but also reduced the side reactions, decreasing the impurity content in the product. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of the welding process on the microstructure and mechanical properties of Au/Sn–3.0Ag–0.5Cu/Cu solder joints. In this study, the flip-chip LED chip and the substrate were connected by a heating platform, hot air reflow, and vacuum reflow soldering methods to form Au/Sn–Ag–Cu/Cu composite solder joints. Sn–3.0Ag–0.5Cu is selected as the solder. The microstructure of the solder joint interface and the inferred surface is studied to characterize the influence of different welding methods on the intermetallic compound (IMC) interface. The void ratio of the flip-chip solder layer was tested to characterize the effect of the effective connection area between the chip and the solder joint on the shear strength. The experimental results show that the void ratio of the solder joints welded by the heating platform is as high as 26%, the IMC interface thickness is higher, and the diffusion coefficient of the solder is the lowest. The void distribution of hot air reflow soldering joints is relatively uniform, and there is less solder residue on the fractured surface. Vacuum reflow soldering has the lowest void rate in the solder joints, and the solder joints have flat cross sections with almost no solder residues. The fracture occurs at the lower layer of solder. Considering comprehensively, when the soldering method is vacuum reflow, the IMC layer of the flip-chip solder joint is the thinnest, and the diffusion coefficient of the solder is the highest. There are almost no voids in the solder layer, and the mechanical properties of the solder joint are the best. At this time, the photo-thermoelectric parameters of the filament are the best, and the performance is the most stable and reliable. [ABSTRACT FROM AUTHOR]

We develop a model of free gift card promotions in a two-product supply chain. We analyze three cases with different gift card strategies based on whether the wholesale and retail prices are changed or unchanged. The results indicate that, in the strategy of unchanged wholesale and retail prices, the retailer and two manufacturers are better off. The gift card promotions can effectively stimulate the demands of two products. We find that the two manufacturers benefit more from the gift card promotion than the retailer does. In the strategy of unchanged wholesale prices and changed retail prices, if the gift card value is not very large, the retailer's profit margins and average consumer surplus from the gift card promotion are increasing in the gift card value. In the strategy of changed wholesale and retail prices, the retailer and two manufacturers have an incentive to raise the retail and wholesale prices under certain conditions. Furthermore, we show that, keeping the prices at the level of no gift cards is the dominant strategy for the supply chain and consumers, and the difference between retail prices and wholesale prices of two products is the key factor in determining the gift card value. [ABSTRACT FROM AUTHOR]

A UVC-LED is a deep ultraviolet LED with a wavelength of 200–280 nm, which has a wide range of applications in disinfection, water purification and other fields. However, UVC-LED chips have limitations of high heat and poor heat dissipation, so it is necessary to test and analyze the thermal and optical properties of UVC-LEDs. This paper studies the photothermal characteristics of a UVC-LED under different driving currents. By measuring and analyzing the radiation flux, radiation intensity, junction temperature and thermal resistance of the UVC-LED under different driving currents, it is found that the junction temperature of the UVC-LED under the transient and steady state increases with the increase in the driving current, while the thermal resistance decreases. The radiation flux and intensity of the UVC-LED increase with the increase in driving current. The peak wavelength appears as a redshift with the increase in current, and the half-wave width also increases with the increase in current. [ABSTRACT FROM AUTHOR]

This article focuses on the study of voids in the solder joint interface of flip-chip LEDs. Additional flux is added to the solder paste to reduce the frequency of voids, and the mechanical properties of the solder joints are compared and analyzed. Two kinds of fluxes (NC559-TPF, TSF-6516) are selected. They are used in two kinds of solder pastes (lead and lead-free solder paste). The amount of flux added is 0 mL, 0.4 mL, and 0.8 mL. The other preparation conditions for all combinations are the same and are within the recommended scope of the manufacturer's documentation. X-rays are used to detect and analyze the distribution and quantity of voids in the solder layer. A push–pull tester is used to analyze and evaluate the mechanical strength of the solder joints. A scanning electron microscope was used to observe the microscopic morphology of the cross-section of the solder joint and the fracture morphology of the interface after the shear test. It was found that adding a proper amount of high-activity flux to the solder paste can reduce the frequency of voids and improve the mechanical reliability of the solder joints. Finally, we tested the thermal performance of the flip-chip LED filament for verification. [ABSTRACT FROM AUTHOR]

With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers' preferences in a timely manner. With the continuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification methods. However, how to choose a trusted method is a difficult problem for companies, because customer reviews (or opinions) contain a lot of uncertain information and noise. This article reports on a detailed survey of recent opinion mining literature. It also reviews how to extract text features in opinions that may contain noise or uncertainties, how to express knowledge in opinions, and how to classify them. Through this extensive study, this paper discusses open questions and recommends future research directions for building the next generation of opinion mining systems. [ABSTRACT FROM AUTHOR]

In the actual production process, soldering is a key step in the flip-chip packaging process. The reliability of the interconnection interface of flip-chip LED chips is considered to be one of the most important reliability issues. This paper studies the influence of different soldering temperatures on the microstructure and shear behavior of the Au/Sn-3.0Ag-0.5Cu/Cu solder joints of flip-chip LED chips. The lead-free solder Sn-3.0Ag-0.5Cu was selected as the solder. The microstructure of the intermetallic compound (IMC) interface and the inferred surface of the solder joint is observed, and the microstructure evolution of the solder joint is analyzed. The void ratio of the solder joints at 250°C, 260°C, 270°C, 280°C, and 290°C soldering temperature was tested to characterize the influence of the contact area between the chip and the solder joint on the shear stress. In addition, the solder joints were aged for 1000 hours in an environment with a relative humidity of 85°C/85%, and a shear test was performed to evaluate the influence of the interface reaction on the mechanical reliability of the solder joints during the isothermal aging process. The research show that when the soldering temperature is 270°C, the Au on the bottom of the chip, and the solder and Cu on the substrate have fully reacted, and the solder joints have high shear resistance. The shear strength of the aging solder joints increases first and then decreases, because the high temperature repairs the defects in the solder layer caused by the low soldering temperature to a certain extent. With the extension of aging, cracks and voids gradually appear at the fracture interface, and the effective contact area decreases. The shear strength of the solder joint decreases, and the fracture mode becomes brittle. [ABSTRACT FROM AUTHOR]

The epigenetic mechanisms coordinating the maintenance of adult cellular lineages and the inhibition of alternative cell fates remain poorly understood. Here we show that targeted ablation of the histone chaperone HIRA in myogenic cells leads to extensive transcriptional modifications, consistent with a role in maintaining skeletal muscle cellular identity. We demonstrate that conditional ablation of HIRA in muscle stem cells of adult mice compromises their capacity to regenerate and self-renew, leading to tissue repair failure. Chromatin analysis of Hira-deficient cells show a significant reduction of histone variant H3.3 deposition and H3K27ac modification at regulatory regions of muscle genes. Additionally, we find that genes from alternative lineages are ectopically expressed in Hira-mutant cells via MLL1/MLL2-mediated increase of H3K4me3 mark at silent promoter regions. Therefore, we conclude that HIRA sustains the chromatin landscape governing muscle cell lineage identity via incorporation of H3.3 at muscle gene regulatory regions, while preventing the expression of alternative lineage genes. The epigenetic mechanisms coordinating the maintenance of adult cellular lineages remain poorly understood. Here the authors demonstrate that HIRA, a H3.3 histone chaperone, establishes the chromatin landscape required for skeletal muscle cell identity. [ABSTRACT FROM AUTHOR]

The basis for the formation of spherical edge burrs during servo valve spool shoulder grinding and a method for eliminating these burrs were investigated. Based on the results of this investigation, a grinding and synchronous deburring device was designed. A geometric model of single abrasive grains was established and the formation mechanism of the valve spool edge burrs was evaluated. Subsequently, the deburring process was analyzed via dynamic simulations, and based on the results, the aforementioned device was designed for mounting on an S20 precision cylindrical grinder. The correctness of the simulation and the feasibility of the device were verified via experiments. The experimentally observed burr shape and size corresponded closely to the simulated features. Moreover, the test device was effective in removing the edge burrs of the valve spool, as evidenced by the lack of sharp edge burrs. Hence, this device could be helpful in grinding the valve spool shoulder for significant improvement in the processing quality and efficiency. [ABSTRACT FROM AUTHOR]

Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing methods is far from ideal. One reason is because of lacking effective criteria to assess the quality of reviews. In this paper, we propose two novel measures, i.e. feature relevance and feature comprehensiveness, to assess the quality of reviews in terms of review content. A review selection approach is presented to select a set of reviews with high quality based on the two measures. Experiments on real-world review datasets show that our proposed method can assess the review quality effectively to improve the performance of review selection. [ABSTRACT FROM AUTHOR]

In the current information era over the internet, social media has become one of the essential information sources for users. While the text is the primary information representation, finding relevant information is a challenging mission for researchers due to its nature (e.g., short length, sparseness). Acquiring high-quality search results from massive data, such as social media needs a set of representative query terms that are not always available. In this paper, we propose a novel query-based unsupervised learning model to represent the implicit relationships in the short text from social media. This bridges the gap of the lack of word co-occurrences without requiring many parameters to be estimated and external evidence to be collected. To confirm the proposed model effectiveness, we compare the proposed model with state-of-the-art lexical, topic model and temporal models on the large-scale TREC microblog 2011-2014 collections. The experimental results show that the proposed model significantly improved overall state-of-the-art lexical, topic model and temporal models with the maximum percentage of increase reaching 33.97% based on MAP value and 21.38% based on Precision at top 30 documents. The proposed model can improve the social media search effectiveness in potential closely retrieval tasks, such as question answering and timeline summarisation. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to evaluate whether baseline 18F-fluorodeoxyglucose (FDG) uptake is associated with carotid plaque progression.Methods: A total of 156 subjects with carotid plaque were enrolled and underwent carotid magnetic resonance imaging (MRI) (at baseline and the 12-month follow-up) and positron emission tomography-computed tomography (PET-CT) (baseline). Carotid plaque progression was evaluated by two indices (the incidence of plaque progression and percentage of plaque increase) with three-dimensional (3D) imaging, while the 18F-FDG uptake was evaluated by the 18F-FDG uptake levels and 18F-FDG uptake velocity. The association between plaque progression and 18F-FDG uptake was investigated by the trend test and multivariate logistic regression analysis.Results: Of the 156 subjects, 80 (51.3%) showed carotid plaque progression during the 12-month follow-up. Firstly, no association was found between 18F-FDG uptake levels and plaque progression. Secondly, significant differences in the incidence of plaque progression were observed among the groups with different uptake velocities, showing a significant decreasing trend ranging from high to intermediate to low (p = 0.002, trend test). After adjusting for covariates, an adequate prediction of the 18F-FDG uptake velocity for the incidence of plaque progression was revealed (OR = 0.682, p < 0.05). In addition, no association was found between the 18F-FDG uptake velocity and the percentage of plaque increase in the subjects with plaque progression (p = 0.757, trend test).Conclusions: Our findings suggest 18F-FDG uptake velocity is independently associated with the incidence of carotid plaque progression. Additionally, the 18F-FDG uptake velocity, as another important parameter of PET-CT, warrants further study in future clinical research.Key Points: • The18F-FDG uptake levels were not associated with the carotid plaque progression. • The18F-FDG uptake velocity could predict the incidence of carotid plaque progression. • The18F-FDG uptake velocity with related factors warrants more attention in future clinical research. [ABSTRACT FROM AUTHOR]

The glucocorticoid receptor (GR) is the main effector of the activation of the hypothalamus-pituitary-adrenal (HPA) axis, which is caused by different types of stress that can be divided into two major categories: physical stress and psychological stress. Given the marked presence of GR in the hippocampus, GR-mediated hippocampal injury might be the core event under stress. The aim of this study was to investigate GR expression, hippocampal injury, and behaviors in rats to explore the differences between these types of stressors. Adult male rats were stressed using a classical model (electrical foot shock and a yoked psychologically stressful situation) to induce physical or psychological stress. The GR expression, injury of hippocampal subfields and behavioral abnormalities were dynamic, as demonstrated using immunofluorescence, 3D magnetic resonance imaging (MRI) and open field exploration (OFE), respectively. In addition, housing in a normal environment for 6 weeks was used to verify the recovery ability of rats. First, GR-mediated hippocampal atrophy and behavioral abnormalities were found in the second week under physical stress, but those changes did not appear until the fourth week under psychological stress. Second, the effects of stress were more pronounced after physical stressors than after psychological stressors in the fourth week, but this trend had reversed by the sixth week, especially in the DG (Dentate Gyrus) subfield. Except for the rats that had experienced 6 weeks of psychological stress, all rats showed significant recovery after 6 weeks of housing in a normal environment. The effects of physical stress appeared early but were relatively moderate, whereas the effects of psychological stress appeared late but were more severe. In addition, GR-mediated serious injury in the DG might be the cause of the DG volume loss and behaviors that could not be reversed. [ABSTRACT FROM AUTHOR]

Novel electronic donor with polycyclic structure based on coumarin was designed and prepared. Pyrrolidine acting as an isolated group was introduced to the donor by the Buchwald-Hartwig amination reaction. Such a novel electronic donor demonstrated great electron-donating capability and an excellent isolation effect. Both chromophores exhibited a D-π-A-π-A molecular structure. Such a structure has great advantages in energy matching, dipole interaction, and electron transfer, compared to the single D-π-A structure. The chemical structures of novel chromophores based on this donor were characterized by 1H nuclear magnetic resonance spectroscopy, 13C nuclear magnetic resonance spectroscopy, and mass spectrometry. The largest electro-optic coefficients are approximately 31.2 and 42.6 pm/V for chromophores NLO-C and NLO-P, respectively, at 1310 nm; otherwise, both chromophores NLO-C and NLO-P exhibit high thermal decomposition temperatures, i.e., 276 and 237 °C, respectively. [ABSTRACT FROM AUTHOR]

Underwater positioning and tracking scheme for non-cooperative objects is of great essence to explore unknown fields. Due to the high response time and non-line-of-sight(NLOS) propagation in the underwater acoustic sensor networks (UASNs), the existed range-based 3D target tracking algorithms are generally inaccurate on detecting underwater non-cooperative objects. In order to solve the problems above, the corresponding solutions are presented respectively in this paper. Although it is hard to change the inherent property of the underwater acoustic propagation, reducing the communication time is another way to solve the problem indirectly. Since the ranging phase and synchronize phase occupy most of the communication time, the presented novel ranging scheme for non-cooperative objects reduces the redundant time consumption, and further eliminates the necessity of synchronization process in advanced. For NLOS propagation, a distributed residual weighting discrimination (DRWD) algorithm based on grouping strategy is proposed for non-cooperative objects. The position estimations of the groups containing the NLOS link error are always distributed in isolation, and the estimations without the NLOS link errors are always concentrated in a small range. According to this feature, a low computational complexity approach namely two-step least square (LS) is proposed to determine the best location by analyzing the distribution of estimated coordinates. Meanwhile, a parameterized selection strategy is proposed first time to evaluate the construction of reference nodes in 3D target tracking. We provide a mathematical proof for our strategy, which avoids the ambiguity occurrence caused by the distribution of reference nodes. The new scheme provided for underwater acoustic tracking (UWAT) greatly improves the positioning accuracy in mixed LOS/NLOS environment. At the end of the paper, simulations are illustrated to evaluate and validate the algorithmic superiority and effectiveness. [ABSTRACT FROM AUTHOR]

Although the FDA already has approved two types of drug therapies for Alzheimer’s disease, in regard to moderate AD, there is no clear research to support the best choice of drug treatment. The goal of this study was to examine the levels of serum biomarkers in moderate-AD patients and to explore the value of these serum biomarkers for the diagnosis of memantine sensitivity in AD patients who are significantly affected by MEM. In our study, 177 patients with moderate AD were enrolled and divided into memantine-sensitive AD (n = 90) and memantine-insensitive AD (n = 87) groups. The sera from all patients were collected, and seven serum biomarkers were analysed. Then, 120 patients were used to establish a diagnostic model that was built with a binary logistic regression analysis, and 57 patients were used to validate our model. In addition, the area under the receiver operating characteristic (ROC) curve was established. From the seven serum biomarkers, the four serum biomarkers that were selected in to establish the regression model were VEGF, BDNF, IL-6 and IL-1β. The ROC curve of best combined detection was 0.899. The diagnostic ratio of the logistic model was 0.825. This study suggests that the logistic regression model (LRM) and the ROC curve based on patients’ serum levels of VEGF, BDNF, IL-6 and IL-1β is a promising research for diagnosing and choosing the best course of treatment for moderate AD. [ABSTRACT FROM AUTHOR]

Patterns are used as a fundamental means to analyse data in many text mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and lack of grounded (e.g. a priori defined) semantics have made it difficult for a user to interpret and explore the patterns. An insight into the meanings of the patterns can benefit users in the process of exploring them. In this regard, this paper presents a model to automatically interpret patterns by achieving two goals: (1) providing the meanings of patterns in terms of ontology concepts and (2) providing a new method for generating and extracting features from an ontology to describe the relevant information more effectively. Taking advantage of a domain ontology and a set of relevant statistics (e.g. term frequency in a document, inverse term frequency in a domain ontology, etc.), our proposed model can give an insight into the hidden meanings of the patterns. The model is evaluated by comparing it with different baseline models on three standard datasets. The results show that the performance of the proposed model is significantly better than baseline models. [ABSTRACT FROM AUTHOR]

This study examines the seasonal connections between the interannual variations in LOD (length of day)/AAM (the relative atmospheric angular momentum for the whole globe) and the ENSO-like SST (El Niño/Southern Oscillation-like sea surface temperature) pattern and corresponding zonal and vertical circulations. Consistent with previous studies, the ENSO-like SST impact the following season LOD/AAM, with the strongest correlations in DJF (December, January, and February), when it is likely to be the peak El Niño/La Niña period. Lag correlations between the interannual variations in LOD/AAM and surface temperature, and the interannual variations in LOD and both zonal circulation and vertical airflow around the equator, consistently indicate that the LOD/AAM reflect the potential impacts of variations in the Earth's rotation rate on the following season's sea surface temperatures (SST) over the tropical central and eastern Pacific (where the ENSO-like SST pattern is located). Moreover, the centers of strongest variation in the AAM (the relative atmospheric angular momentum for an air column and the unit mass over a square meter) are located over the mid-latitudinal North Pacific in DJF and MAM (March, April, and May), and over the mid-latitudinal South Pacific in JJA (June, July, and August) and SON (September, October, and November). This suggests that the AAM over the mid-latitudinal Pacific around 30°N (30°S) dominate the modulation of Earth's rotation rate, and then impact the variations in LOD during DJF and MAM (JJA and SON). [ABSTRACT FROM AUTHOR]

Skeletal muscle fibers are mainly categorized into red and white fiber types, and the ratio of red/white fibers within muscle mass plays a crucial role in meat quality such as tenderness and flavor. To better understand the molecular difference between the two muscle fibers, this study takes advantage of RNA-seq to compare differences in the transcriptome between extensor digitorum longus (EDL; white fiber) and soleus (Sol; red fiber) muscles of large white pigs. In total, 89,658,562 and 46,723,568 raw reads from EDL and Sol were generated, respectively. Comparison between the two transcriptomes revealed 561 differentially expressed genes, with 408 displaying higher and 153 lower levels of expression in Sol. Quantitative real-time polymerase chain reaction validated the differential expression of nine genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis discovered several differentially enriched biological functions and processes of the two muscles. Moreover, transcriptome comparison between EDL and Sol identified many muscle-related genes (CSRP3, ACTN2, MYL1, and MYH6) and pathways related to myofiber formation, such as focal adhesion, tight junction formation, extracellular matrix (ECM)-receptor pathway, calcium signaling, and Wnt signaling. In addition, 58,362 and 58,359 single nucleotide polymorphisms were identified in EDL and Sol, respectively, and the sequence of 9069 genes was refined at the 5′, 3′ or both ends. Numerous novel transcripts and alternatively spliced RNAs were also identified. Our transcriptome analysis constitutes valuable sequence resource for uncovering important genes and pathways involved in muscle fiber type determination, and might help further our understanding of the molecular mechanisms in different types of muscle. [ABSTRACT FROM AUTHOR]

GaN thin films and multi-quantum wells (MQWs) were grown on the c-axis patterned sapphire substrates and c-axis flat sapphire substrates by metal organic chemical vapor deposition, respectively. The surface morphology of patterned sapphire substrate and flat sapphire substrate were measured by scanning electron microscopy. The crystal structure of GaN thin films and MQWs were measured by X-ray. The optical performance of MQWs was measured by photoluminescence spectra. The residual stress of GaN thin films was studied. GaN thin films on patterned sapphire substrate has a better crystalline quality in the [102] direction than that on flat sapphire substrate. The residual stress in MQWs on PSS is δ = 1.10 GPa, δ = 0.13 GPa, while the residual stress of films on FSS is δ = 0.83 GPa, δ = 0.10 GPa. It is found that the wavelength becomes shorter and the emission intensity becomes stronger on patterned sapphire substrate. [ABSTRACT FROM AUTHOR]

The catalytic behavior of ultra-low-gold-loaded Au/CeO during CO oxidation was studied using HRTEM, TGA, in-situ FTIR, CO-TPD, O-TPD, and O-TPO. Herein, water, which facilitates CO oxidation, is present on the catalyst rather than obtained from the reactant gas. The experimental results show that the presence of water on the catalyst enhances the adsorption and activation of O to produce O on the catalyst surface, resulting in excellent catalytic activity. Different reaction pathways for CO oxidation over Au/CeO may be present; however, the dominant pathway is influenced by the species on the catalyst surface. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Traceability system in the food supply chain is becoming more necessary. RFID and EPCglobal Network Standards are emerging technologies that bring new opportunities to develop the high performance traceability system. This research proposes the analysis, design, and development of the RFID and EPCglobal Network Standards based traceability system that adheres to the requirements of global food traceability in terms of completeness of traceability information. The additional components, including lot management system and electronic transaction management system, encourage the traditional system in order to fulfill the missing information. The proposed system was developed and applied in a rice supply chain. Results from experimentation showed that the additional components can significantly improve the completeness of traceability information. The collaboration between EPCglobal Network Standards and electronic transaction management system can improve the performances in RFID operations. [ABSTRACT FROM AUTHOR]

This paper provides a review of paleoclimate modeling activities in China. Rather than attempt to cover all topics, we have chosen a few climatic intervals and events judged to be particularly informative to the international community. In historical climate simulations, changes in solar radiation and volcanic activity explain most parts of reconstructions over the last millennium prior to the industrial era, while atmospheric greenhouse gas concentrations play the most important role in the 20th century warming over China. There is a considerable model-data mismatch in the annual and boreal winter temperature change over China during the mid-Holocene [6000 years before present (ka BP)], while coupled models with an interactive ocean generally perform better than atmospheric models. For the Last Glacial Maximum (21 ka BP), climate models successfully reproduce the surface cooling trend over China but fail to reproduce its magnitude, with a better performance for coupled models. At that time, reconstructed vegetation and western Pacific sea surface temperatures could have significantly affected the East Asian climate, and environmental conditions on the Qinghai-Tibetan Plateau were most likely very different to the present day. During the late Marine Isotope Stage 3 (30-40 ka BP), orbital forcing and Northern Hemisphere glaciation, as well as vegetation change in China, were likely responsible for East Asian climate change. On the tectonic scale, the Qinghai-Tibetan Plateau uplift, the Tethys Sea retreat, and the South China Sea expansion played important roles in the formation of the East Asian monsoon-dominant environment pattern during the late Cenozoic. [ABSTRACT FROM AUTHOR]

The reduction of hippocampal volume remains controversial in depression because of the variability among individuals in clinical studies. Here, a reliable experimental rat model of depression, established by chronic unpredictable mild stress (CUMS), was used. Thirty rats were randomly divided into two groups (CUMS group and control group). Hippocampal volume was dynamically measured every 2 weeks in a 56-day chronic stress procedure using structural magnetic resonance imaging, and the correlation between the hippocampal volume and the learning and memory changes was investigated. Our results demonstrated that CUMS rats showed significantly smaller volumes of the bilateral hippocampus compared to that of the controls, changing dramatically with the development of CUMS procedure. The left hippocampal volume was reduced earlier and more markedly than the right one from the 2nd week to the 8th week of the CUMS procedure (on the 8th week: left: approximately 15.3 %; right: approximately 8.4 % reduction). Additionally, the hippocampal volume of CUMS rats was significantly negatively correlated with the learning and memory changes. Of note, it showed that the more obviously the hippocampal volume reduced, the more severely the learning and memory damaged. In conclusion, the hippocampal volume decreased gradually and dynamically and was correlated with the impairment of the learning and memory in depression. [ABSTRACT FROM AUTHOR]