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In an era of unprecedented technological advancement, the power industry is undergoing a transformative evolution, particularly in intelligent power grid enterprises. The integration of cutting-edge information science and technology has ushered in a new era of automation, informatization, and intelligence within these enterprises. While this progression promises enhanced production, operation, and management capabilities, it also brings forth a daunting challenge: the effective governance of the burgeoning volumes of power data. This paper aims to conduct in-depth research on evaluating the effectiveness of data governance in power grid enterprises based on deep learning to integrate more closely with their business systems. The main objective is to provide effective and convenient intelligent services for decision-making within an innovative power grid enterprise management system and strengthen the data architecture of these enterprises in data management. First, the deep learning neural network's principle structure and training methods are introduced in detail and combined with the deep learning neural network. This is a different evaluation model for the data governance effectiveness of power grid enterprises based on penalty variable weight. The difference probability density of the power difference data series in the power grid is taken as the evaluation index. The evaluation model for the governance effectiveness of different data is modified. Build a different evaluation model of power grid enterprise data governance effectiveness based on punishment and weight change, comprehensively consider the extent to which the data volume of power grid abnormal data in power grid enterprises affects the evaluation of data governance effectiveness, and complete the assessment of power grid enterprise data governance effectiveness based on deep learning. Experimental results underscore the method's efficacy, demonstrating an exceptional accuracy rate of 94%. This empirical validation highlights the method's efficient evaluation process, offering invaluable technical support for enhancing power data management's consistency, precision, and reliability within power grid enterprises. Moreover, comparative analyses against other methodologies, including KNN, SVM, RF, DT, and RNN, reaffirm the superiority of the DNN model, solidifying its outstanding performance. [ABSTRACT FROM AUTHOR]

Objectives: To determine the incremental diagnostic value of radiomics signature of pericoronary adipose tissue (PCAT) in addition to the coronary artery stenosis and plaque characters for detecting hemodynamic significant coronary artery disease (CAD) based on coronary computed tomography angiography (CCTA). Methods: In a multicenter trial of 262 patients, CCTA and invasive coronary angiography were performed, with fractional flow reserve (FFR) in 306 vessels. A total of 13 conventional quantitative characteristics including plaque characteristics (N = 10) and epicardial adipose tissue characteristics (N = 3) were obtained. A total of 106 radiomics features depicting the phenotype of the PCAT surrounding the lesion were calculated. All data were randomly split into a training dataset (75%) and a testing dataset (25%). Then three models (including the conventional model, the PCAT radiomics model, and the combined model) were established in the training dataset using multivariate logistic regression algorithm based on the conventional quantitative features and the PCAT radiomics features after dimension reduction. Results: A total of 124/306 vessels showed functional ischemia (FFR ≤ 0.80). The radiomics model performed better in discriminating ischemia from non-ischemia than the conventional model in both training (area under the receiver operating characteristic (ROC) curve (AUC): 0.770 vs 0.732, p < 0.05) and testing datasets (AUC: 0.740 vs 0.696, p < 0.05). The combined model showed significantly better discrimination than the conventional model in both training (AUC: 0.810 vs 0.732, p < 0.05) and testing datasets (AUC: 0.809 vs 0.696, p < 0.05). Conclusions: The PCAT radiomics model showed good performance in predicting myocardial ischemia. Addition of PCAT radiomics to lesion quantitative characteristics improves the predictive power of functionally relevant CAD. Key Points: • Based on the plaque characteristics and EAT characteristics, the conventional model showed poor performance in predicting myocardial ischemia. • The PCAT radiomics model showed good prospect in predicting myocardial ischemia. • When combining the radiomics signature with the conventional quantitative features (including plaque features and EAT features), it showed significantly better performance in predicting myocardial ischemia. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the antibacterial properties of nine lactic acid bacterial strains. The agar diffusion method (Oxford cup method) was used to assess the antimicrobial activity against pathogenic bacteria in aquaculture. The results showed that all selected strains inhibited the growth of Photobacterium damsel, Vibrio alginolyticus, Listonella anguillarum, Edwardsiella ictaluri, Aeromonas hydrophila, V. arahaemolyticus, Edwardsiella tarda and V. harveyi, but did not inhibit the growth of Metschnikowia bicuspidata. Among these strains of lactic acid bacteria, r1 was selected for its obvious antibacterial activity against eight kinds of pathogenic bacteria by 96-well plate method. Its inhibitory rate reached 96%, and it could inhibit the growth of six pathogenic bacteria at 121 ℃, 20 min and pH 3.5–4.0. It was identified as Lactiplantibacillus plantarum by morphological observation and 16S rRNA sequencing analysis. Because strain r1 was isolated from culture ponds and exerted strong inhibitory effects on pathogenic bacteria, it holds potential as an agent to prevent and control infectious diseases in aquaculture. This study provides a foundation for the development and utilization of probiotics in aquaculture. [ABSTRACT FROM AUTHOR]

Simply constructing multiple responsive polymers with obvious shape and dimension variations on their assemblies upon different stimuli is still rarely reported. In this study, we report a hyperbranched polymer named HPAzoBAHB-star-PEG9 with quadruple-response to light, temperature, pH and oxidation stimuli. The polymer contains azobenzene chromophore, sulfide, amide and amine groups in its hydrophobic hyperbranched core, and the core is capped with hydrophilic polyethylene glycol (PEG9) arms. HPAzoBAHB-star-PEG9 could assemble into unusual leaf-like lamellar micelles at 25 °C under the guidance of orderly arranged H-aggregate of azobenzene moieties. These leaf-like lamellar micelles can transform into vesicles upon UV irradiation and lower temperature, or convert to smaller spherical micelles in acidic or oxidative environments, respectively, with the destroy of ordered azobenzene arrangements. This quadruple-responsive hyperbranched polymer is suitable to construct multiple stimuli-responsive micro/nanostructures, or accurate delivery and release following subtle stimuli sequences. [ABSTRACT FROM AUTHOR]

The change detection (CD) technology has greatly improved the ability to interpret land surface changes. Deep learning (DL) methods have been widely used in the field of CD due to its high detection accuracy and application range. DL-based CD methods usually cannot fuse the extracted feature information at full scale, leaving out effective information, and commonly use transfer learning methods, which rely on the original dataset and training weights. To address the above issues, we propose a deeply supervised (DS) change detection network (DASUNet) that fuses full-scale features, which adopts a Siamese architecture, fuses full-scale feature information, and realizes end-to-end training. In order to obtain higher feature information, the network uses atrous spatial pyramid pooling (ASPP) module in the coding stage. In addition, the DS module is used in the decoding stage to exploit feature information at each scale in the final prediction. The experimental comparison shows that the proposed network has the current state-of-the-art performance on the CDD and the WHU-CD, reaching 94.32% and 90.37% on F1, respectively. [ABSTRACT FROM AUTHOR]

With the development of data science, more and more data are presented in the form of multi-view. Compared with single-view feature learning, multi-view feature learning is more effective, and it has been successfully applied in many fields. Clustering is a core technology of computer science. Thus, many researchers start to study multi-view clustering. Recently, combining with multi-view feature learning techniques, some multi-view clustering methods have been presented. These methods mainly focus on the multiple features fusion, while most of them ignore the correlations among multiple views. Therefore, it cannot make full use of the advantages of multiple view features. In this paper, we propose a novel approach, named multi-view clustering via neighbor domain correlation learning (MCNDCL) approach. Specifically, MCNDCL learns a discriminant common space for multiple view features. Under the learned common space, the correlations of the consistent neighbor domain are maximized, and the correlations of specific neighbor domain are minimized at the same time. Extensive experimental results on four typical benchmarks, i.e., UCI Digits, Caltech7, BBCSport and CCV, validate the high effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Biolog EcoPlates are useful tools to study the metabolic function of microbial community in water bodies; however, the influence of aquaculture water, which contains complex chemical compositions, on color development in Biolog EcoPlates, has not been clearly illustrated. Previously, we detected a delayed color development in original water samples, comparing to that in the tenfold diluted samples. The objective of this study was to evaluate the differences of average well color development (AWCD) and community-level physiological profiles (CLPP) between the original and tenfold diluted water samples collected from three intensive Litopenaeus vannamei culture ponds, and to explore potential causes of the delayed color development by measuring microbial community structure and physiochemical indexes. Compared with the tenfold diluted samples, the original water samples from all three ponds exhibited delayed color development and reduced microbial metabolic activity. The original water samples exhibited higher utilization of carbohydrates, while significantly higher utilization of amino acids was observed in the corresponding diluted samples. The CLPP of the original water samples was significantly correlated with the microbial community structure at 48 h of incubation, meanwhile both CLPP and community structure were substantially influenced by NO3–N and NO2–N. Therefore, NO2-N and NO3-N accumulation in the intensive Litopenaeus vannamei culture pond might be responsible for the delayed color development in Biolog EcoPlates. In order to minimize the influence of aquaculture water on color development in Biolog EcoPlates, a shorter incubation period is recommended. [ABSTRACT FROM AUTHOR]

Image dark data, whose content and value are not clear, consistently occupy the storage space but hardly produce great value. Blindly applying data mining techniques on these data is highly likely to bring disappointed result and waste large resource. Therefore, it is of great significance to assess the dark data before data mining to help the user cognize the data. However, there are several challenges in dark data assessment work. First, the similarity between images must be objectively measured under aunified standard to help the user understand the evaluation values of dark data. Second, it is important to capture semantic features with generalization ability. Third, it is challenging to design an efficient assessment scheme to support large-scale datasets. To overcome these challenges, we propose an assessment framework which includes offline calculation and online assessment. In offline calculation, we first transform unlabeled images into hash codes by our developed Deep Self-taught Hashing (DSTH) algorithm which can extract semantic features with generalization ability, then construct a semantic graph using restricted Hamming distance, and finally use our designed Semantic Hash Ranking (SHR) algorithm to calculate the overall importance score (rank) for each node (image), which takes both the number of connected links and the weight on edges into consideration. During online assessment, we first translate the user's query (semantic images) into hash codes using DSTH model, then match the data contained in the dark data via a predefined Hamming distance query range, and finally return the weighted average value of these matched data to help the user cognize the dark data. The results on real-world dataset show our framework can apply to large-scale datasets, help users evaluate the dark data by different requirements, and assist the user to conduct subsequent data mining work. [ABSTRACT FROM AUTHOR]

The cross-domain characteristic of cloud storage service decides that both users and service providers have limited trust toward each other. Judging from a real-world perspective, both parties may have the motivation to engage in dishonest activity for monetary reasons. Hence, accountability should be seriously treated in designing storage systems with practical security. This paper proposes a general accountable framework for cloud storage in a data-centric manner. We design non-repudiable action records to log all data-related access behavior, and through later auditing to detect possible misbehavior. To resist replay attacks, we adopt signature exchange idea to let both parties verify and maintain different metadata signatures signed by the other party. For potential disputes about data content or access records, we also design arbitration protocol to fairly and efficiently settle the dispute and find out the cheating party. Experimental evaluation of our prototype shows that cryptographic cost, storage overhead and throughput are reasonable and acceptable. [ABSTRACT FROM AUTHOR]

Linear and star-shaped polylactides (PLA) with similar molecular weights of each arm are synthesized via ring-opening polymerization of LA with 3-butyn-1-ol and pentaerythritol as initiators, respectively. By solution blending of equivalent mass of poly(L-lactic acid)s (PLLAs) and poly(D-lactic acid)s (PDLAs), perfect PLA stereocomplexes (scPLAs) are prepared and confirmed by WAXD and FTIR analysis. Effect of chain architectures on stereocomplex crystallization is investigated by studying the non-isothermal and isothermal crystallization of linear and star-shaped polylactide stereocomplexes. In dynamic DSC and POM test, star-shaped PLLA (4sPLLA)/PDLA and PLLA/star-shaped PDLA (4sPDLA) stereocomplexes reach rapid crystallization and higher crystallinity due to larger spherulite density of star-shaped chain and excellent chain mobility of linear chain. In isothermal crystallization test, much faster crystallization and less crystallization half-time is obtained with the increase of star-shaped chain. Meanwhile, 4sPLLA/PDLA and PLLA/4sPDLA are found to have the highest crystallinity, suggesting limitation of too much star-shaped chain for 4sPLLA/4sPDLA and restriction of linear chain in nucleation capacity for PLLA/PDLA. The results reveal that star-shaped chain has an important influence on the crystallization of scPLAs. [ABSTRACT FROM AUTHOR]

In this paper, we consider the ( L, 1) state-dependent reflecting random walk (RW) on the half line, which is an RW allowing jumps to the left at a maximal size L. For this model, we provide an explicit criterion for (positive) recurrence and an explicit expression for the stationary distribution. As an application, we prove the geometric tail asymptotic behavior of the stationary distribution under certain conditions. The main tool employed in the paper is the intrinsic branching structure within the ( L, 1)-random walk. [ABSTRACT FROM AUTHOR]

Insecure attachment is regarded as a risk factor for depressive symptoms. However, insecure attachment can be divided into attachment avoidance and anxiety, so a better understanding of the relationship between attachment avoidance, attachment anxiety, and depressive symptoms is needed. The current study applied a structural equation model to verify our hypothesis that different facets of affective empathy mediate the relationship between attachment avoidance, attachment anxiety, and depressive symptoms. The participants of this study included four hundred and sixty-four undergraduate students who completed the Experiences in Close Relationships-Relationship Structures Questionnaire, Interpersonal Reactivity Index, and Center for Epidemiologic Studies Depression Scale. The results indicated that the correlation between attachment avoidance and depressive symptoms was mediated by empathic concern, while the correlation between attachment anxiety and depressive symptoms was mediated by personal distress. These results implied that attachment avoidance and attachment anxiety work on depressive symptoms through different pathways. [ABSTRACT FROM AUTHOR]

The correlations between the structural and electronic properties of the monolayer clusters M (where M = Ni, Pd, Pt) and the sandwich complexes M(CR) (where M = Ni, Pd, Pt; R = H, F) were studied by performing quantum-chemical calculations. All of the sandwich complexes are strongly donating and backdonating metal-ligand bonding structures. The influence of the ligand as well as significant variations in the M-C, M-M, and C-C bond lengths and binding energies were examined to obtain a qualitative and quantitative picture of the intramolecular interactions in CR-M. Our theoretical investigations show that the binding energies of these sandwich complexes gradually decrease from Ni to Pt as well as from H to F, which can be explained via the frontier orbitals of the clusters M and CR. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Multicellular magnetotactic prokaryotes (MMPs) are a group of aggregates composed of 7-45 gram-negative cells synthesizing intracellular magnetic crystals. Although they are thought to be globally distributed, MMPs have been observed only in marine environments in America and Europe. Most MMPs share a rosette-like morphology and biomineralize iron sulfide crystals. In the present study, abundant MMPs were observed, with a density of 26 ind./cm, in the sediments of a coastal lagoon, Lake Yuehu, in the Yellow Sea. Optical microscopy showed that all of them were rosette shaped with a diameter of 5.5±0.8 μm. Transmission electron microscopy revealed that these MMPs were composed of 10-16 ovoid cells and flagellated peritrichously. High-resolution transmission electron microscopy and energy dispersive X-ray analysis indicated that they biomineralized bullet-shaped magnetite crystals in highly organized parallel chains within which the magnetosomes were oriented in the same direction. This is the first report of MMPs from Asia and demonstrates the ubiquitous distribution of MMPs. [ABSTRACT FROM AUTHOR]

The control of particulate matter (PM) emissions from coal combustion becomes an urgent work due to their adverse effects on human health. Coal blending is a promising option for submicron particulate (PM1) reduction. This study addressed the effects of coal blending on the formation and properties of particulate matter in combustion process. Coal blends from lignite and bituminous coal, with different blend ratios (9:1, 7:3, 5:5, 3:7 and 1:9), were combusted in a drop tube furnace. The mass size distribution, concentration, elemental composition and morphology of the particulate matter generated under O2/N2 and O2/CO2 conditions were characterized. Particulate matter was collected by a low pressure impactor (LPI), which aerodynamically segregated particulates into thirteen fractions with sizes ranging from 0.03 to 9.8 µm. The results showed that coal blending reduced PM1 generation, compared with the calculated average values from the combustion of constituent coals. This indicated that the mineral interactions had a great effect on PM1 reduction. The blend ratio also played an important role in the suppression of PM1 generation. In this experimental study, PM1 generation suffered a maximum suppression at the blend ratio of 7:3. The O2/CO2 atmosphere affected the formation and properties of the PM1 during coal blends combustion. Compared with the O2/N2 combustion, the interaction of minerals was weakened under O2/CO2 combustion, thus the suppression of PM1 generation decreased after coal blending. Compared with the calculated values, the concentrations and percentages of Ca, Fe in PM1 decreased, but the concentrations of Ca, Fe, Si and Al in coarse particulates (PM10+) increased after coal blends combustion. The interactions between the aluminosilicates in the bituminous coal and volatile elements Ca, Fe in the lignite were thought to contribute to the suppression of PM1 generation during the combustion of coal blends. [ABSTRACT FROM AUTHOR]

The aligned molecular packing structure is vital to the anisotropic charge transport in conjugated polymer and small molecule thin films. However, how this molecular packing motif influences the photoelectric conversion process at the donor/acceptor heterojunction is still mysterious. Herein, we employed a PM6/Y6 bilayer model to investigate the long-range alignment of molecular packing induced photoelectric conversion process. Both PM6 and Y6 layers were properly controlled to exhibit the uniaxially oriented molecular packing compared to their as-cast counterparts, as revealed by the polarized absorption spectra and transmission electron microscopy. After analyzing the photovoltaic performance of bilayer devices, the smaller energy loss, lower energetic disorder, and longer charge carrier lifetime can be observed in the bilayer devices with aligned Y6 molecules, which contribute to a higher power conversion efficiency (PCE) than the as-cast devices. While the molecular packing structure of PM6 layer exhibited negligible influence on the device performance, probably resulting from the intrinsic semicrystalline nature of PM6 molecules. Our results indicate that the alignment of small molecular acceptor at the donor/acceptor interfaces should be a powerful strategy to facilitate the photoelectric conversion process, which will definitely pave the way to highly efficient bulk heterojunction photovoltaic device. [ABSTRACT FROM AUTHOR]

Subarachnoid hemorrhage (SAH) is one of the life-threatening neurosurgical diseases in central nervous system. Autophagy has been previously demonstrated to exert vital roles in SAH development. Angiotensin I converting enzyme 2 (ACE2) has been revealed as a regulator of autophagy in neurosurgical diseases. However, effect of ACE2 on autophagy in SAH progression has not been clarified. First, we explored the relationship between autophagy and SAH progression by establishing a mouse model of SAH under the administration of 3-MA (the autophagy inhibitor). Next, we examined ACE2 expression in the cerebral cortex of SAH mice ex vivo with RT-qPCR. Subsequently, we assessed the biological function of ACE2 on brain injury, the autophagic flux pathway and the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling ex vivo via neurological scoring, TUNEL assay, western blot analysis and immunofluorescence staining assay. Finally, we carried out rescue assays under chloroquine (CQ, the autophagic flux inhibitor) and LY294002 (the PI3K/AKT signaling inhibitor) administration. 3-MA mitigated brain injury after SAH, and ACE2 was downregulated in cerebral cortex of SAH mice. Moreover, ACE2 elevation alleviated cell apoptosis, cerebral edema, and neurological deficits, ameliorated the autophagic flux pathway and activated the PI3K/AKT signaling in SAH mice. Furthermore, CQ and LY294002 neutralized the effects of overexpressed ACE2 on neuronal apoptosis, cerebral edema, and neurological deficits in SAH mice. Overall, ACE2 lessened neuronal injury via the autophagic flux and PI3K/AKT pathways. This research might provide a potential novel direction for clinical treatment of SAH. [ABSTRACT FROM AUTHOR]

Well-shaped sulfated titania nanofibers were prepared by impregnation of hydrogen titanate with (NH4)2S2O8 and calcination. Various characterization results showed that sulfated titania nanofibers consisted of smooth-surface and long-size fibers with the diameter of dozens of nanometers. Moreover, acid characteristics of sulfated titania nanofibers exhibited the high acid content, multi-strength acid sites and large Brønsted/Lewis acid ratio. In low-viscosity esterification, sulfated titania nanofibers had 1.2 ~ 2.1 times the reaction rate of other sulfated titania and other typical solid acids. Simultaneously, sulfated titania nanofibers possessed the higher DS value of product than those by other solid acids in highly viscous cellulose acetylation (2.41 versus 2.06 ~ 2.21). Besides these, sulfated titania nanofibers could show the excellent recycling performance in viscous acid-catalytic reactions. [ABSTRACT FROM AUTHOR]

In order to improve the life of gun barrel influenced by periodic transient thermal shock during firing, it is necessary to establish the heat conduction model of gun barrel to study the temperature field and its variation rule. Therefore, a mathematical model of two-dimensional nonlinear heat conduction is established. The governing equations and boundary conditions are linearized by Kirchhoff's variation, and the finite difference equations of internal nodes and boundary nodes are derived using energy balance method and alternating difference implicit scheme. Based on the numerical results of the classics interior ballistic, temperature distribution of some 12.7 mm machine gun barrel during 120 successive firing rounds under the firing specification of the GJB3484-98 is calculated numerically. The temperature field of the external surface of the barrel is tested and the variation law of the temperature field is obtained. Comparison with experimental results shows good agreement with the simulation. The research results provided scientific basic for the studies of new barrel materials and coatings. [ABSTRACT FROM AUTHOR]

As a common technology in social network, clustering has attracted lots of research interest due to its high performance, and many clustering methods have been presented. The most of existing clustering methods are based on unsupervised learning. In fact, we usually can obtain some/few labeled samples in real applications. Recently, several semi-supervised clustering methods have been proposed, while there is still much space for improvement. In this paper, we aim to tackle two research questions in the process of semi-supervised clustering: (i) How to learn more discriminative feature representations to boost the process of the clustering; (ii) How to effectively make use of both the labeled and unlabeled data to enhance the performance of clustering. To address these two issues, we propose a novel semi-supervised clustering approach based on deep metric learning (SCDML) which leverages deep metric learning and semi-supervised learning effectively in a novel way. To make the extracted features of the contribution of data more representative and the label propagation network more suitable for real applications, we further improve our approach by adopting triplet loss in deep metric learning network and combining bedding with label propagation strategy to dynamically update the unlabeled to labeled data, which is named as semi-supervised clustering with deep metric learning and graph embedding (SCDMLGE). SCDMLGE enhances the robustness of metric learning network and promotes the accuracy of clustering. Substantial experimental results on Mnist, CIFAR-10, YaleB, and 20-Newsgroups benchmarks demonstrate the high effectiveness of our proposed approaches. [ABSTRACT FROM AUTHOR]

Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity. To further understand functional organization in the resting human brain, a statistical physics approach called the eigen-microstate analysis is applied to functional MRI data, revealing a small set of leading basic modes that dominate rich repertoires of spontaneous activity. [ABSTRACT FROM AUTHOR]

The use of advanced thermal spray technology for surface modification of aluminum alloy parts for automobiles can greatly improve the surface properties and service life of the parts while saving resources and reducing environmental pollution. In this paper, a Ni60A coating was prepared on the surface of aluminum alloy 109 (ZL109) by supersonic plasma spraying (HEPJet). The spraying process parameters were optimized based on the response surface methodology (RSM). The effects of the main factors such as spraying power, spraying distance, and Ar flow rate on the porosity and microhardness of the coating were analyzed by the Box–Behnken method (BBD). The response surface of the porosity and microhardness of the coating was constructed by establishing the appropriate mathematical model. The response surface of porosity and microhardness of the coating was constructed by establishing a relevant mathematical model. The results show that the established mathematical model is reliable, and the spray power has the greatest influence on the porosity and microhardness of the coating. The optimal porosity and microhardness of the coating predicted by the model are 0.85% and 971.1HV0.1, respectively, which is close to the experimental results. The porosity and microhardness of the coating measured by the experiment are 0.87% and 959.8HV0.1, respectively. The coatings prepared with optimal parameters have good compactness, small porosity, and high hardness. [ABSTRACT FROM AUTHOR]

Quantization-based hashing methods have become increasingly popular to adjust the global data distribution and accurately capture the data similarity compared with pairwise/triplet similarity-based methods. However, the existing image quantization hashing approaches adopt fixed hash centers, which consider neither the semantic information of each hash center nor the scale size of each object appearing in a multi-label image, resulting in that each hash code will deviate from its corresponding hash centroid. To address this issue, we propose HCCST, a hash centroid construction method with Swin transformer for multi-label image retrieval. HCCST consists of a hash code generation module, a hash centroid construction module and an interaction module between each hash code and its corresponding hash centroid. In the hash code generation module, we first adopt Swin transformer to extract the feature vector for each input multi-label image and then generate the initialized hash code of this image. In the hash centroid construction module, we first utilize the object semantic information to construct semantic hash centers and then consider the object scale size by learning the object weight coefficient to compute the hash centroid for each sample. After obtaining both the hash code and hash centroid of each sample, in the last interaction module, we constantly limit the distance between each hash code and its hash centroid to preserve the similarity between samples. Our model will be trained in an end-to-end manner to alternately update the net parameters of hash code generation module, hash centroid construction module and the object weight coefficient. We conduct extensive experiments on 3 multi-label image datasets including VOC2012, MS-COCO and NUS-WIDE. The experimental results demonstrate that HCCST can achieve better retrieval performance compared with the state-of-the-art image hashing methods. The open-source code of this project is released at: https://github.com/lzHZWZ/HCCST.git. [ABSTRACT FROM AUTHOR]

Visual chirality reveals the phenomenon that chiral data will present different semantics after flipping. Although image flipping is widely used in image hashing learning as a data augmentation technique, the effect of learning chiral image data on hashing performance has not been fully discussed. To explore this issue, this paper first designs an approach to recognize images with chiral cues, then constructs the chiral datasets including different proportions of images with chiral cues, and finally analyzes and discusses the performance change via testing three representative image hashing methods with different hash code lengths on constructed chiral datasets. In addition, to understand the effect of visual chirality from an internal perspective, we illustrate visual results of activated regions between some original images with chiral cues and their flipped ones. We conduct the above experiments on three public image datasets including VOC2007, MS-COCO, and NUS-WIDE. Experimental results reveal that different proportions of chiral data will greatly affect the performance of image hashing and the best performance appears when the proportion of images with chiral cues accounts for 15% ∼ 25% or 75% ∼ 85%. The code of this work is released at: https://github.com/lzHZWZ/Visual_Chirality_Hashing. [ABSTRACT FROM AUTHOR]

Providencia genus is known to harbor certain opportunistic pathogens capable of causing human infections. Here, we report two strains of multidrug-resistant bacteria initially identified as Providencia rettgeri by mass spectrometry, but genome analysis revealed their ANI (79.84–84.20%) and dDDH (21.1–25.6%) values to fall below the accepted species threshold for known Providencia species. We therefore propose that these isolates be recognized as a novel species, Providencia xianensis sp. nov. Alarmingly, both strains, isolated from locations far apart, exhibited resistance to last-resort antibiotics, indicating their possible wide distribution, underscoring the urgency for immediate attention and enhanced surveillance for this emerging multidrug-resistant pathogen. [ABSTRACT FROM AUTHOR]

We prove that the local times of a sequence of Sinai's random walks converge to those of Brox's diffusion by proper scaling. Our proof is based on the intrinsic branching structure of the random walk and the convergence of the branching processes in random environment. [ABSTRACT FROM AUTHOR]

Attribute-based encryption(ABE) can enable user-centered data sharing in untrusted cloud scenario where users usually lack control on their outsourced data. However, existing ABE schemes have intrinsic limitations on scalability and revocation efficiency due to the bottleneck of a central authority and heavy re-encryption overhead on revocations. In this paper, we present a revocable decentralized attribute-based encryption scheme for data access control in cloud storage. In particular, by integrating decentralized attribute-based encryption, key regression technique, all-or-nothing transform, revocation list for involved attributes, and blacklist in a novel way, we provide a revocable ABE scheme with practical dynamic group membership and identity privacy protection, and meanwhile, it enhances the re-encryption efficiency caused by revocations without sacrificing security. We analyzed the security of our scheme. The experimental evaluation demonstrates that the cryptographic overhead on key derivation, encryption(decryption), and ABE ciphertext update are reasonable, and for the throughput of accessing encrypted data from the cloud, our scheme outperforms other schemes. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether radiomics signature of pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could improve the prediction of future acute coronary syndrome (ACS) within 3 years. Methods: We designed a retrospective case-control study that patients with ACS (n = 90) were well matched to patients with no cardiac events (n = 1496) during 3 years follow-up, then which were randomly divided into training and test datasets with a ratio of 3:1. A total of 107 radiomics features were extracted from PCAT surrounding lesions and 14 conventional plaque characteristics were analyzed. Radiomics score, plaque score, and integrated score were respectively calculated via a linear combination of the selected features, and their performance was evaluated with discrimination, calibration, and clinical application. Results: Radiomics score achieved superior performance in identifying patients with future ACS within 3 years in both training and test datasets (AUC = 0.826, 0.811) compared with plaque score (AUC = 0.699, 0.640), with a significant difference of AUC between two scores in the training dataset (p = 0.009); while the improvement of integrated score discriminating capability (AUC = 0.838, 0.826) was non-significant. The calibration curves of three predictive models demonstrated a good fitness respectively (all p > 0.05). Decision curve analysis suggested that integrated score added more clinical benefit than plaque score. Stratified analysis revealed that the performance of three predictive models was not affected by tube voltage, CT version, different sites of hospital. Conclusion: CCTA-based radiomics signature of PCAT could have the potential to predict the occurrence of subsequent ACS. Radiomics-based integrated score significantly outperformed plaque score in identifying future ACS within 3 years. Key Points: • Plaque score based on conventional plaque characteristics had certain limitations in the prediction of ACS. • Radiomics signature of PCAT surrounding plaques could have the potential to improve the predictive ability of subsequent ACS. • Radiomics-based integrated score significantly outperformed plaque score in the identification of future ACS within 3 years. [ABSTRACT FROM AUTHOR]

Serial dependence (SD) is a phenomenon wherein current perceptions are biased by the previous stimulus and response. This helps to attenuate perceptual noise and variability in sensory input and facilitates stable ongoing perceptions of the environment. However, little is known about the developmental trajectory of SD. This study investigates how the stimulus and response biases of the SD effect develop across three age groups. Conventional analyses, in which previous stimulus and response biases were assessed separately, revealed significant changes in the biases over time. Previous stimulus bias shifted from repulsion to attraction, while previous response bias evolved from attraction to greater attraction. However, there was a strong correlation between stimulus and response orientations. Therefore, a generalized linear mixed-effects (GLME) analysis that simultaneously considered both previous stimulus and response, outperformed separate analyses. This revealed that previous stimulus and response resulted in two distinct biases with different developmental trajectories. The repulsion bias of previous stimulus remained relatively stable across all age groups, whereas the attraction bias of previous response was significantly stronger in adults than in children and adolescents. These findings demonstrate that the repulsion bias towards preceding stimuli is established early in the developing brain (at least by around 10 years old), while the attraction bias towards responses is not fully developed until adulthood. Our findings provide new insights into the development of the SD phenomenon and how humans integrate two opposing mechanisms into their perceptual responses to external input during development. [ABSTRACT FROM AUTHOR]

Numerosity perception is a fundamental and innate cognitive function shared by both humans and many animal species. Previous research has primarily focused on exploring the spatial and functional consistency of neural activations that were associated with the processing of numerosity information. However, the inter-individual variability of brain activations of numerosity perception remains unclear. In the present study, with a large-sample functional magnetic resonance imaging (fMRI) dataset (n = 460), we aimed to localize the functional regions related to numerosity perceptions and explore the inter-individual, hemispheric, and sex differences within these brain regions. Fifteen subject-specific activated regions, including the anterior intraparietal sulcus (aIPS), posterior intraparietal sulcus (pIPS), insula, inferior frontal gyrus (IFG), inferior temporal gyrus (ITG), premotor area (PM), middle occipital gyrus (MOG) and anterior cingulate cortex (ACC), were delineated in each individual and then used to create a functional probabilistic atlas to quantify individual variability in brain activations of numerosity processing. Though the activation percentages of most regions were higher than 60%, the intersections of most regions across individuals were considerably lower, falling below 50%, indicating substantial variations in brain activations related to numerosity processing among individuals. Furthermore, significant hemispheric and sex differences in activation location, extent, and magnitude were also found in these regions. Most activated regions in the right hemisphere had larger activation volumes and activation magnitudes, and were located more lateral and anterior than their counterparts in the left hemisphere. In addition, in most of these regions, males displayed stronger activations than females. Our findings demonstrate large inter-individual, hemispheric, and sex differences in brain activations related to numerosity processing, and our probabilistic atlas can serve as a robust functional and spatial reference for mapping the numerosity-related neural networks. [ABSTRACT FROM AUTHOR]

Differentially private spatial decompositions split the whole domain into sub-domains recursively to generate a hierarchical private tree and add Laplace noise to each node's points count. However the Laplace distribution is symmetric about the origin, the mean of a large number of queries may cancel the Laplace noise and reveal privacy. Existing methods take the solution by limiting the number of queries. But in private tree the points count of intermediate node may be real since the summation of all its descendants may cancel the Laplace noise. To address these problems of differentially private spatial decompositions, we propose a more secure algorithm to make the Laplace noise not be canceled. That splits the domains depending on its real points count not noisy, and only adds indefeasible Laplace noise to leaves. That the i th randomly selected leaf of one intermediate node is added noise by β − i + 1 + 1 + β (β − i + 1) + β L a p (λ) . We also propose the definition of Lapmin(λ) whose absolute value is not greater than Sensitivity(f). It is proved that adding Lapmin(λ) noise to query answer guarantees both differential privacy and minimal relative error comparing with unlimited Laplace noise. The experiment results show that our algorithm performs better both on synthetic and real datasets with higher security and data utility, and the noises costs is highly decreased. [ABSTRACT FROM AUTHOR]

A novel hierarchically N-doped porous carbon material (NP) has been prepared using potassium citrate as the carbon source and urea as the nitrogen source by a facile one-step carbonization method. The resulting NP materials possess two important characteristics: (1) They have abundant nitrogen contents (up to 11.03%) and (2) they exhibit, in relation to porous carbon without urea doping (narrow micropore size), a wider mesopore size. The NP materials exhibit an excellent acetone adsorption capacity; a highest value of 1058 mg g−1 (15 °C) was provided by carbon material prepared in the case of urea/potassium citrate = 1 and 800 °C, which is 158.6% higher than that of the N-free material. The total pore volume, especially the mesoporous volume, was the key factor to determine acetone adsorption under relative high pressure due to the presence of a multilayer adsorption based on adsorption isotherm models. Meanwhile, nitrogen functional groups promoted adsorption process at relative low pressure, and density functional theory results further confirm nitrogen-containing functional groups enhance adsorption interaction between carbon surface and acetone molecule through the hydrogen bonding interaction. This study paves a new way to develop a novel hierarchically N-doped porous carbon with controllable well-developed porosity for the adsorption applications. [ABSTRACT FROM AUTHOR]

To enhance the anti-friction and anti-wear properties of the WC/TiC/Co carbide, micro-EDM was selected to fabricate textured micro-holes on the carbide surface, and graphite was embedded into the micro-holes to form self-lubricating tools. Dry machining tests on AISI 1045 hardened steel were carried out with the self-lubricating tool and conventional ones. The cutting temperature and tool wear were investigated and compared. The results showed that the self-lubricating tool embedded with graphite exhibited excellent efficiency and stability in reducing cutting temperature, and then the tool wear was reduced compared to that of the conventional one. Through the analysis of test results and cutting temperature distribution theory, the mechanisms responsible are put forward: the first one is the formation of a graphite film on the tool rake face, which is conducive to reducing the temperature of chip and tool caused by tool-chip friction and chip deformation; the other one is attributed to the reduced actual contact length at the tool-chip interface due to the micro-holes, which is propitious to lower the temperature of chip and tool caused by tool-chip friction, supply more graphite, and store chip debris. [ABSTRACT FROM AUTHOR]

To improve the tribological properties of the cemented carbide, Ti-MoS/Zr composite coatings were deposited on the substrate surface by medium-frequency magnetron sputtering coupled with multi-arc ion plating. The morphology and fundamental properties of the coatings were examined. Dry machining test on AISI 1045 hardened steel was carried out with the coated tool and uncoated tool. The variation of cutting temperature was tested by a TH5104 thermo tracer. The results showed that cutting speed had a profound effect on the cutting temperature. In low-speed cutting of less than 120 m/min, the cutting temperature was decreased by 18-22% and flank wear resistance was improved by 25-30% in comparison with that of uncoated tool, while in high-speed cutting, there was not much difference in cutting performance owing to the high temperature caused by the elevated cutting speed. Through the analysis of cutting heat distribution theory, two responsible mechanisms are found: as lubricating additive between the tool and chip sliding couple, the Ti-MoS/Zr composite coatings contribute to the decrease of average shear stress on the rake face and the thermal barrier effect in heat conduction. These may result in the decrease of the cutting temperature of tool and chip, and hence the improvement of tool wear resistance. [ABSTRACT FROM AUTHOR]

Background: Apatinib, a third-line or later treatment for advanced gastric cancer (aGC), was shown to improve overall survival and progression-free survival (PFS) compared with placebo in the phase III trial. Given the modest benefit with high costs, we further evaluated the cost-effectiveness of apatinib for patients with chemotherapy-refractory aGC. Methods: A Markov model was developed to simulate the disease process of aGC (PFS, progressive disease, and death) and estimate the incremental cost-effectiveness ratio (ICER) of apatinib to placebo. The health outcomes and utility scores were derived from the phase III trial and previously published sources, respectively. Total costs were calculated from the perspective of the Chinese health-care payer. Sensitivity analysis was used to explore model uncertainties. Results: Treatment with apatinib was estimated to provide an incremental 0.09 quality-adjusted life years (QALYs) at an incremental cost of $8113.86 compared with placebo, which resulted in an ICER of $90,154.00 per QALY. Sensitivity analysis showed that across the wide variation of parameters, the ICER exceeded the willingness-to-pay threshold of $23,700.00 per QALY which was three times the Gross Domestic Product per Capita in China. Conclusions: Apatinib is not a cost-effective option for patients with aGC who experienced failure of at least two lines chemotherapy in China. However, for its positive clinical value and subliminal demand, apatinib can provide a new therapeutic option. [ABSTRACT FROM AUTHOR]

Diamond-Cu composites have been considered to be the next generation of electronic packing materials. One of the key stumbles for such an application is the joining problem between diamond-Cu composites and other materials due to the poor wettability of the diamond particles in the composites. In order to overcome this hurdle, pure Cu powder was thermally sprayed onto diamond-Cu substrate by low-temperature high-velocity oxygen fuel spraying process. Microstructure and some fundamental properties of the coating obtained were systematically investigated, and morphologies of the single splat deposited on the diamond-Cu substrate were also observed. The splats obtained have good adhesion with the substrate as fine particles flattened sufficiently, while the coarse particles were significantly deformed. The coating was quite dense with porosity lower than 1%, oxygen content under 0.5% and thermal conductivity about 266 Wm K and still remained on the diamond-Cu substrate after 50 thermal shock cycles between 300 °C and water bath at room temperature. Meanwhile, the solderability of the coating was significantly improved. Therefore, Cu coating deposited on diamond-Cu substrate by low-temperature high-velocity oxygen fuel spraying process can be beneficial in electronic industry assisting with soldering and improved wettability for joining of other materials. [ABSTRACT FROM AUTHOR]

Intestinal failure (IF)-associated liver disease (IFALD), as a major complication, contributes to significant morbidity in pediatric IF patients. However, the pathogenesis of IFALD is still uncertain. We here investigate the roles of bile acid (BA) dysmetabolism in the unclear pathogenesis of IFALD. It found that the histological evidence of pediatric IF patients exhibited liver injury, which was characterized by liver bile duct proliferation, inflammatory infiltration, hepatocyte apoptosis and different stages of fibrosis. The BA compositions were altered in serum and liver of pediatric IF patients, as reflected by a primary BA dominant composition. In IF patients, the serum FGF19 levels decreased significantly, and were conversely correlated with ileal inflammation grades (r = −0.50, p < 0.05). In ileum, the inflammation grades were inversely associated with farnesoid X receptor (FXR) expression (r = −0.55, p < 0.05). In liver, the expression of induction of the rate-limiting enzyme in bile salt synthesis, cytochrome P450 7a1 (CYP7A1) increased evidently. In conclusion, ileum inflammation decreases FXR expression corresponding to reduce serum FGF19 concentration, along with increased hepatic bile acid synthesis, leading to liver damages in IF patients. [ABSTRACT FROM AUTHOR]

Currently, research on content based image copy detection mainly focuses on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very time-consuming and unscalable. Hence, we need to pay much attention to the efficiency of image detection. In this paper, we propose a fast feature aggregating method for image copy detection which uses machine learning based hashing to achieve fast feature aggregation. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach. [ABSTRACT FROM AUTHOR]

Background and aims: Organic amendments are an option in enhancing soil biological productivity. Limited research exists on the effects of long-term cattle manure addition on low-molecular-weight organic acids (LMWOAs) concentrations in corn ( Zea mays L) and soybean ( Glycine max L.) fields in the region of soil erosion. The purpose of this study was to investigate the potential influence of cattle manure on the LMWOAs concentration in erosion soil. Methods: A field experiment was established in Hailun city, Northeast China to determine the impact of long-term cattle manure addition on the total amounts of main LMWOAs in eroded Mollisol fields. There were three levels of simulated-erosion, which removed 0, 10 and 30 cm of topsoil. Two soil amendments were: (1) chemical fertilizer at the rate normally used by farmers in the region and (2) chemical fertilizer plus 15,000 kg ha (dry weight basis) of cattle manure. Main LMWOAs in soil were assessed at the flowering stage of soybean and the jointing stage of corn. Principle results: Compared to chemical fertilizer alone, 7-years of repeated cattle manure addition significantly increased total amounts of main LMWOAs in rhizosphere about 9-70 times and bulk soil about 6-62 times. The magnitude of increase by cattle manure was in the order of oxalate>malate>malonate>lactate>maleate in corn plots, and oxalate>malate>malonate>lactate in soybean plots. In comparison, cattle manure and topsoil removal had larger effect on LMWOAs concentrations in corn plot than soybean plot. The addition of cattle manure application and top soil removal had significant independent influence on main LMWOAs concentration in soil solution. Conclusion: This study suggested that addition of cattle manure would be an effective approach in modifying soil biological properties through the increases in low-molecular-weight organic acids to eroded Chinese Mollisols. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the relationship between cell cycle reentry and apoptosis in cultured cortical neurons following oxygen-glucose deprivation (OGD). We found that the percentage of neurons with BrdU uptake, TUNEL staining, and colocalized BrdU uptake and TUNEL staining was increased relative to control 6, 12 and 24 h after 1 h of OGD. The number of neurons with colocalized BrdU and TUNEL staining was decreased relative to the number of TUNEL-positive neurons at 24 h. The expression of phosphorylated retinoblastoma protein (phospho-Rb) was significantly increased 6, 12 and 24 h after OGD, parallel with the changes in BrdU uptake. Phospho-Rb and TUNEL staining were colocalized in neurons 6 and 12 h after OGD. This colocalization was strikingly decreased 24 h after OGD. Treatment with the cyclin-dependent kinase inhibitor roscovitine (100 μM) decreased the expression of phospho-Rb and reduced neuronal apoptosis in vitro. These results demonstrated that attempted cell cycle reentry with phosphorylation of Rb induce early apoptosis in neurons after OGD and there must be other mechanisms involved in the later stages of neuronal apoptosis besides cell cycle reentry. Phosphoralated Rb may be an important factor which closely associates aberrant cell cycle reentry with the early stages of neuronal apoptosis following ischemia/hypoxia in vitro, and pharmacological interventions for neuroprotection may be useful directed at this keypoint. [ABSTRACT FROM AUTHOR]

Nanoparticles are thought to induce more severe health impacts than larger particles. The nanopartides from coal-fired boilers are classified into three size fractions with a 13-stage low pressure impactor. Their physicochemical properties are characterized by the high-resolution field emission scanning electron microscope and X-ray fluorescence spectrometer (XRF). The results show that coal-derived nanoparticles mainly consist of individual primary particles of 20-150 nm and their aggregates. Inorganic nanoparticles primarily contain ash-forming elements and their aggregates have a dense structure. Organic nanoparticles are dominated by the element carbon and their aggregates have a loose structure. Nanoparticles from the same boiler have a similar composition and are primarily composed of sulfur, refractory elements and alkali/alkaline elements. Some transition and heavy metals are also detected. For different boilers, greater differences are observed in the production of the nanoparticles and their composition, possibly due to the use of low-NOx, burners. Coal-derived nanoparticles have a small size, large specific surface area and complicated chemical composition, and thus are potentially more harmful to human health. [ABSTRACT FROM AUTHOR]

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of 99mTc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis. This AI model demonstrated considerable diagnostic performance, the areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians individually classifying bone metastasis. Further AI-consulted interpretation also improved human diagnostic sensitivity and accuracy. In total, this AI model performed a valuable benefit for nuclear medicine physicians in timely and accurate evaluation of cancer bone metastasis. [ABSTRACT FROM AUTHOR]

Considering the poor medical conditions in some regions of China, this paper attempts to develop a simple and easy way to extract and process the bone features of blurry medical images and improve the diagnosis accuracy of osteoporosis as much as possible. After reviewing the previous studies on osteoporosis, especially those focusing on texture analysis, a convexity optimization model was proposed based on intra-class dispersion, which combines texture features and shape features. Experimental results show that the proposed model boasts a larger application scope than Lasso, a popular feature selection method that only supports generalized linear models. The research findings ensure the accuracy of osteoporosis diagnosis and enjoy good potentials for clinical application. [ABSTRACT FROM AUTHOR]

The quasielastic scattering angular distribution of 17C from a carbon target at 40MeV/nucleon was measured using the inverse kinematics method. The analysis of the experimental data was started using the optical model. The data were reproduced very well and the optical potential in the Woods-Saxon form was obtained. Within the framework of the coupled-channel method, the contribution from the inelastic channels to the experimental data is found significant at large angles. A systematic analysis of the elastic/quasielastic scattering of 11-13,17C + 12C was performed as well. The present work indicates that 17C is a normal nucleus whose radius is not anomalously large. [ABSTRACT FROM AUTHOR]