Multiple Types Of Information Research Articles

Some applications of Scanning Electron Microscopy to the study of reservoir rock

Scanning electron microscopy (SEM) is a powerful tool for visualising reservoir pore and grain framework systems which (SEM) provides qualitative information about pore geometry through direct observation of a rock or a pore cast of the rock. This aids in understanding reservoir productivity capabilities. The SEM is useful for locating and identifying minerals, particularly clay minerals - an aid when designing drilling and completion programs. SEM unlike conventional light microscopy, produces images by recording various signals resulting from interactions of an electron beam with the sample as it is scanned in a raster pattern across the sample surface. Combined with a backscatter detector (BSE), energy dispersal X-ray spectroscopy (EDS), and EDS - Mapping, SEM can yield multiple types of information about geological samples at the same time, such as superficial microstructure, BSE image, component analysis, and crystal structure features. In addition, new technic of SEM like Qemscan and FIB (Focused ion beam) can automatically minerals mapping and approach to 3-D imaging. In this paper, we use granite rock from a granitic basement reservoir, Bach Ho Oilfield as examples to discuss the geological application of SEM. The most important reservoir properties of granite rock in the oilfield are porosity and fractured pores which are abundant. The fractures have various sizes from mm to micron in width. Some large cracks have 1÷3 cm in lenght and 100÷600 µm in width. The small ones have 0.001÷0.2 mm in length and 10÷100 µm in width. Some large fractures were porefilled by crystals that have blade shape.

High-Confidence Sample Generation Technology and Application for Global Land-Cover Classification

Deep learning technology has become one of the most important technologies in remote sensing land classification applications. Its powerful sample-learning and information-mining abilities promote the continuous improvement of classification accuracy. A large volume of high-quality and representative sample data is the premise for the successful application of deep learning technology. Conventional methods of obtaining samples through manual delineation or surface surveys require a great deal of manpower and material resources. Therefore, the inability to obtain sufficient and widely representative high-quality samples is one of the key factors limiting the application of deep learning technology. In this study, the method of generating sample data obtains high-confidence classification results from a variety of existing high-quality classification products as deep learning samples, which are then used to support the application of deep learning technology for land-cover classification. When the three global land-cover classification products, FROM-GLC-2015, GLC_FCS30-2015, and GlobeLand30, have the same type of discrimination, the sample is considered a high-confidence sample. Based on this, a large volume of sample data widely distributed around the world was obtained. Using the extracted samples, a random forest classifier was trained using multiple types of information from the Landsat data, and land-cover classification was achieved. Application experiments were conducted in several typical regions, and the classification results were verified. The results showed that the classification accuracy of random forests under the support of samples generated using the sample extraction method proposed in this article was considerably higher than that of the three land-cover classification products.

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study.

Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography (CEM) images. In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system (MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion (AFF) algorithm that could intelligently incorporates multiple types of information from CEM images. The average free-response receiver operating characteristic score (AFROC-Score) was presented to validate system's detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases, comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists' performance. On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909 [95% confidence interval (95% CI): 0.822-0.996] and 0.912 (95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists' average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance. MDCS demonstrated excellent performance in the detection and classification of breast lesions, and greatly enhanced the overall performance of radiologists.

Drug response prediction using graph representation learning and Laplacian feature selection

Knowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response experiments are time-consuming and expensive, utilizing human genomic information and drug molecular characteristics to predict drug responses is of urgent importance. Although a variety of computational drug response prediction methods have been proposed, their effectiveness is still not satisfying. In this study, we propose a method called LGRDRP (Learning Graph Representation for Drug Response Prediction) to predict cell line-drug responses. At first, LGRDRP constructs a heterogeneous network integrating multiple kinds of information: cell line miRNA expression profiles, drug chemical structure similarity, gene-gene interaction, cell line-gene interaction and known cell line-drug responses. Then, for each cell line, learning graph representation and Laplacian feature selection are combined to obtain network topology features related to the cell line. The learning graph representation method learns network topology structure features, and the Laplacian feature selection method further selects out some most important ones from them. Finally, LGRDRP trains an SVM model to predict drug responses based on the selected features of the known cell line-drug responses. Our five-fold cross-validation results show that LGRDRP is significantly superior to the art-of-the-state methods in the measures of the average area under the receiver operating characteristics curve, the average area under the precision-recall curve and the recall rate of top-k predicted sensitive cell lines. Our results demonstrated that the usage of multiple types of information about cell lines and drugs, the learning graph representation method, and the Laplacian feature selection is useful to the improvement of performance in predicting drug responses. We believe that such an approach would be easily extended to similar problems such as miRNA-disease relationship inference.

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users’ information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k-RR and RAPPOR to disturb users’ ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.

‘Good for you!’ Processing social emotions in L2 discourse comprehension: an ERP study

AbstractSocial factors impact sentence comprehension in a first language (L1), suggesting that semantic processing cannot be dissociated from social and moral emotions in relation to pro/antisocial individuals. Given that integrating multiple types of information and processing emotion-laden pragmatic information is costlier in a second language (L2), we investigated whether social factors would affect discourse comprehension similarly in L2. Processing the outcomes of scenarios involving pro/antisocial protagonists provoked similar neural patterns in L2 as in L1 (Rodríguez-Gómez, Martín-Loeches, Colmenares, Romero Ferreiro & Moreno, 2020), suggesting that L2 users simultaneously integrate semantic and discourse-pragmatic information during sentence comprehension.

A Survey of Undergraduate Engineering Students' Personal Information Management Practices

ABSTRACTPersonal information management (PIM) includes the activities and preferences that support future information use. PIM is especially important in the context of engineering education, where learners are expected to engage with a substantial amount of information. This paper builds on the findings of a research project on the information practices of undergraduate engineering students, examined through multiple qualitative and quantitative methods (Dodson, 2021). This paper reports on one aspect of the project, an online questionnaire (n = 103). Findings suggest learners regularly engage in PIM activities, and experience different degrees of challenge when managing multiple types of information.

Multiple types of navigational information are independently encoded in the population activities of the dentate gyrus neurons

The dentate gyrus (DG) plays critical roles in cognitive functions, such as learning, memory, and spatial coding, and its dysfunction is implicated in various neuropsychiatric disorders. However, it remains largely unknown how information is represented in this region. Here, we recorded neuronal activity in the DG using Ca2+ imaging in freely moving mice and analyzed this activity using machine learning. The activity patterns of populations of DG neurons enabled us to successfully decode position, speed, and motion direction in an open field, as well as current and future location in a T-maze, and each individual neuron was diversely and independently tuned to these multiple information types. Our data also showed that each type of information is unevenly distributed in groups of DG neurons, and different types of information are independently encoded in overlapping, but different, populations of neurons. In alpha-calcium/calmodulin-dependent kinase II (αCaMKII) heterozygous knockout mice, which present deficits in spatial remote and working memory, the decoding accuracy of position in the open field and future location in the T-maze were selectively reduced. These results suggest that multiple types of information are independently distributed in DG neurons.

The interaction of multiple information on multiplex social networks

Coupled information diffusion in complex networks has been widely studied in recent years. Nevertheless, current research mainly focuses on the interaction between each information pair. In this study, we investigate the interaction of multiple types of information on multiplex networks by considering both the competition and the cooperation among them. To study the dynamic characteristics theoretically, a microscopic Markov chain approach is used to reveal the co-evolution of multiple information. Through extensive simulations, the outbreak threshold is analyzed theoretically. The results reveal that the pairwise interaction between each information pair has an obvious impact on its final outbreak scale and the diffusion threshold. Interestingly, even information that has no direct impact on the target information can affect the diffusion of the target information through indirect effects. In addition, the inhibitory effect of the competitive information and the promotion effect of the cooperative information on the target information will reach equilibrium under specific parameter space conditions. We also conduct numerical simulations on three real multiplex social networks, including two large-scale networks. Current results are beneficial for us to further understand the coupled diffusion of multiple information on multiplex social networks.

Fast screening and confirmation of 73 common prohibited compounds in cosmetics by ultra performance liquid chromatography-quadrupole-time-of-flight high resolution mass spectrometry

建立了超高效液相色谱-四极杆-飞行时间高分辨质谱(UPLC-Q-TOF HRMS)同时快速筛查确证化妆品中73种常见禁用物质的方法。样品经饱和氯化钠溶液分散均匀后,采用含0.2%甲酸的乙腈溶液超声提取,50 mg PSA净化,以8000 r/min高速冷冻离心除脂,采用Waters Acquity HSS T3色谱柱(100 mm×2.1 mm, 1.8 μm)分离。采用多反应监测高分辨扫描模式(MRM HR),以保留时间、一级母离子精确质量数、同位素丰度比和二级子离子精确质量数实现化妆品中73种禁用物质的快速筛查和确证,基质匹配外标法定量。实验比较了不同提取溶剂、净化吸附剂、色谱条件和质谱扫描模式对73种禁用物质测定的影响,并考察了膏霜剂和水剂的基质效应。结果表明,73种禁用物质线性关系良好,相关系数(R2)>0.99;检出限为5~150 μg/kg;定量限为15~450 μg/kg;膏霜剂及水剂两种基质在3个加标水平下的回收率为60.3%~130.3%,日内、日间RSD分别为0.8%~10.0%(n=6)和1.1%~15.0%(n=3)。日常风险监测中检出磺胺甲基异噁唑、甲基泼尼松、林可霉素、对乙酰氨基酚、甲氧苄啶、阿法骨化醇、倍他米松戊酸酯、溴莫尼定、氯霉素、氯苯那敏、氯倍他索丙酸酯、克罗米通、益康唑、酮康唑、泼尼松醋酸酯和泼尼松,检出含量范围为0.5~1136.1 mg/kg。该方法准确、快速、简便,可用于化妆品中73种常见禁用物质的检测。

Emotional conversation generation with heterogeneous graph neural network

The successful emotional conversation system depends on sufficient perception and appropriate expression of emotions. In a real-life conversation, humans firstly instinctively perceive emotions from multi-source information, including the emotion flow hidden in dialogue history, facial expressions, audio, and personalities of speakers. Then, they convey suitable emotions according to their personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, in this paper, we propose a heterogeneous graph-based model for emotional conversation generation. Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i.e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback. Secondly, we employ an Emotion-Personality-Aware Decoder to generate a response relevant to the conversation context as well as with appropriate emotions, through taking the encoded graph representations, the predicted emotions by the encoder and the personality of the current speaker as inputs. Experiments on both automatic and human evaluation show that our method can effectively perceive emotions from multi-source knowledge and generate a satisfactory response. Furthermore, based on the up-to-date text generator BART, our model still can achieve consistent improvement, which significantly outperforms some existing state-of-the-art models.

Optically Sensitive and Magnetically Identifiable Supraparticles as Indicators of Surface Abrasion.

Identifying and ensuring the integrity of products plays an important role in today's globalized world. Miniaturized information taggants in the packaging surface are therefore required to monitor the product itself instead of applying external labels. Ideally, multiple types of information are stored in such additives. In this work, micrometer-sized core-shell particles (supraparticles) were developed to provide material surfaces with both an identifier and a surface abrasion indication functionality. The core of the supraparticles contains iron oxide nanoparticles that allow identification of the surface with a spectral magnetic code resolved by magnetic particle spectroscopy. The fluorescent silica nanoparticles in the supraparticle shell can be abraded by mechanical stress and resolved by fluorescence spectroscopy. This provides information about the mechanical integrity of the system. The application as surfaces, that contain several types of information in one supraparticle, was demonstrated here by incorporating such bifunctional supraparticles as additives in a surface coating.

Identifying opportunities for expert-mediated triangulation in monitoring wildlife trade on social media.

Wildlife trade has rapidly expanded on social media platforms in recent years, offering an easy means for traders to access international markets. Investigating this trade activity poses a complex challenge to researchers seeking to understand online trade and moderators seeking to disrupt illicit and harmful activity. Current survey methods frequently rely on text-based searches and focus on posts in which the advertisement is explicit. However, such approaches risk overlooking a growing volume of relevant content, particularly outside social media groups. We used posts from pages promoting West African birds for trade as a case study to explore the availability of information for making inferences about trade activity on social media, specifically information indicating that trade activity was occurring or that could be used to infer trade routes. We recorded 400 posts from 12 pages that we inferred either promoted or facilitated wildlife trade, of which 19.7% were explicit advertisements and 23.8% contained taxa-related terms. In the remaining 341 posts, profile information was the most common indicator of trade activity, but a variety of indicators (e.g., images of birds in trade and trade enquiries) were identified across imagery, text, and comments. We identified multiple types of geographical information that could help infer trade routes and thus the likely legality of trade, although most were relatively rare and sometimes contradictory. Our findings suggest that triangulating multiple types of information from within, across, and beyond posts is vital for effectively identifying and interpreting wildlife trade content on social media. Therefore, were commend that expert-mediated triangulation should be integrated in and used alongside automated detection systems and moderating practices of social media companies.

Developing a near-infrared spectroscopy and microwave-induced thermoacoustic tomography-based dual-modality imaging system.

Near-infrared spectroscopy (NIRS) techniques can provide noninvasive in vivo hemoglobin oxygenation information but suffer from relatively low resolution in biological tissue imaging. Microwave-induced thermoacoustic tomography (TAT) can produce high-resolution images of the biological tissue anatomy but offer limited physiological information of samples because of the single species of the chromophore it maps. To overcome these drawbacks and take advantage of the merits of the two independent techniques, we built a dual-modality system by combining a NIRS system and a TAT system to image biological tissues. A series of phantom trials were carried out to demonstrate the performance of the new system. The spatial resolution is about 1mm, with a penetration depth of at least 17.5mm in the human subject. A cohort of five healthy subjects was recruited to conduct real-time forearm venous and arterial cuff occlusion experiments. Numerous results showed that this dual-modality system could measure oxygen metabolism and simultaneously provide anatomical structure changes of biological tissues. We also found that although the hemoglobin concentration varied consistently with many other published papers, the TAT signal intensity of veins showed an opposite variation tendency in the venous occlusion stage compared with other existing work. A detailed explanation is given to account for the discrepancy, thus, providing another possibility for the forearm experiments using TAT. Furthermore, based on the multiple types of information afforded by this dual-modality system, a pilot clinical application for the diagnosis of anemia is discussed.

Mathematical model and calculation of safety and stability evaluation of underground space engineering by TOPSIS and analytic hierarchy process

The development of economy promotes the development of urbanization, and the power of urban space engineering, so the stability research of urban underground space engineering is very important. Although the existing evaluation models can consider a single form of underground space safety problems, they seldom consider the uncertainty of cross-blending of multiple information types within a certain interval, and cannot continuously express the quantitative results of the suitability assessment of urban underground space. This paper takes an underground tunnel in China as the research background, calculates the weight of the selected construction stability index through the analytic hierarchy process, and finally evaluates the stability of different sections of the project by TOPSIS method, and draws the corresponding conclusions.

Weighted Integration of Duration Information Across Visual and Auditory Modality Is Influenced by Modality-Specific Attention.

We constantly integrate multiple types of information from different sensory modalities. Generally, such integration is influenced by the modality that we attend to. However, for duration perception, it has been shown that when duration information from visual and auditory modalities is integrated, the perceived duration of the visual stimulus leaned toward the duration of the auditory stimulus, irrespective of which modality was attended. In these studies, auditory dominance was assessed using visual and auditory stimuli with different durations whose timing of onset and offset would affect perception. In the present study, we aimed to investigate the effect of attention on duration integration using visual and auditory stimuli of the same duration. Since the duration of a visual flicker and auditory flutter tends to be perceived as longer than and shorter than its physical duration, respectively, we used the 10 Hz visual flicker and auditory flutter with the same onset and offset timings but different perceived durations. The participants were asked to attend either visual, auditory, or both modalities. Contrary to the attention-independent auditory dominance reported in previous studies, we found that the perceived duration of the simultaneous flicker and flutter presentation depended on which modality the participants attended. To further investigate the process of duration integration of the two modalities, we applied Bayesian hierarchical modeling, which enabled us to define a flexible model in which the multisensory duration is represented by the weighted average of each sensory modality. In addition, to examine whether auditory dominance results from the higher reliability of auditory stimuli, we applied another models to consider the stimulus reliability. These behavioral and modeling results suggest the following: (1) the perceived duration of visual and auditory stimuli is influenced by which modality the participants attended to when we control for the confounding effect of onset–offset timing of stimuli, and (2) the increase of the weight by attention affects the duration integration, even when the effect of stimulus reliability is controlled. Our models can be extended to investigate the neural basis and effects of other sensory modalities in duration integration.

Trends and gaps in precision health research: a scoping review

ObjectiveTo determine progress and gaps in global precision health research, examining whether precision health studies integrate multiple types of information for health promotion or restoration.DesignScoping review.Data sourcesSearches in Medline (OVID),...

DDBN: Dual detection branch network for semantic diversity predictions

It is well known that detail features and context semantics are conducive to improving object detection performance. However, the current single-prediction detectors do not well incorporate these two types of information together. To alleviate the limitation of single-prediction on the use of multiple types of information, we propose a dual detection branch network (DDBN) with adjacent feature compensation and customized training strategy for semantic diversity predictions. Different from the conventional single-prediction models, our DDBN is in the form of a single model with dual different semantic predictions. In particular, two types of adjacent feature compensations are designed to extract detail and context information from different perspectives. Also, a specialized training strategy is customized for our DDBN to well explore the diversity of predictions for improving the performance of object detection. We conduct extensive experiments on three datasets, i.e., DOTA, MS-COCO, and Pascal-VOC, and the experimental results strongly demonstrate the efficacy of our proposed model.

Impact of Multiple Types of Information on Airport OutboundPassengers' Traffic Mode Selection

Impact of Multiple Types of Information on Airport OutboundPassengers' Traffic Mode Selection

Multi-view group representation learning for location-aware group recommendation

With the development of location-based services (LBS), many location-based social sites like Foursquare and Plancast have emerged. People can organize and participate in group activities on those sites. Therefore, recommending venues for group activities is of practical value. However, the group decision making process is complicated, requiring trade-offs among group members. And the data sparsity and cold-start problems make it difficult to make effective group recommendation. In this manuscript, we propose a Multi-view Group Representation Learning (MGPL) framework for location-aware group recommendation. The proposed multi-view group representation learning framework can leverage multiple types of information for deep representation learning of group preferences and incorporate the spatial attributes of locations to further capture the group mobility preferences. Experiments on two real datasets Foursqaure and Plancast show that our method significantly outperforms the-state-of-art approaches.